Exploring V2X in 5G networks: A comprehensive survey of location-based services in hybrid scenarios

Vehicular Communications 52 (2025) 100878 Available online 13 January 2025 2214-2096/© 2025 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). Exploring V2X in 5G networks: A comprehensive survey of location-based services in hybrid scenarios ☆ Bruno Mendes a,b,* , Marco Araújo a , Adriano Goes a , Daniel Corujo b , Arnaldo S.R. Oliveira b a Capgemini, Rua Serpa Pinto, 44, Vila Nova de Gaia 4400-012, Portugal b Instituto de Telecomunicaç˜ oes and Universidade de Aveiro, Campus Universit´ ario de Santiago, Aveiro 3810-193, Portugal ARTICLE INFO Keywords: NR-U V2X ITS C-V2X CAM GNSS Hybrid Positioning ABSTRACT Vehicle-to-Everything (V2X) communications are constrained by both 3GPP technical specifications, as well as by country-specific spectrum regulations. The world’s largest economies, such as the USA, EU and China have self-imposed regulations regarding the specific bandwidths and central spectrum frequencies where both safety and non-safety related V2X communication services are allowed to occur (always aligned with the aforementioned 3GPP technical specifications). Although the channels used for safety, non-safety, and control packets differ, what all of these countries have in common is that V2X shall occur mostly on New Radio Unlicensed (NRU) spectrum, i.e., by means of private networks. A specific bandwidth in the public spectrum is also available, but since public spectrum is purchased through auctions, it is quite common the case that one particular operator will own the entirety of this spectrum, leading to a monopoly in V2X operations. Besides, this public spectrum is quite limited in bandwidth. This of course includes all of the Intelligent Transportation Systems (ITS) services, even location-based services, such as the ones that require the usage of positioning technologies, like autonomous vehicles, that require said services in order to support complex maneuvers and cooperative driving. Global Navigation Satellite Systems (GNSS) such as GPS or Galileo, currently already offer high-accuracy location to vehicles. However, this form of stand-alone position estimation of the vehicle has several drawbacks, as the information is constrained to the individual vehicle and not shared with others in a secure manner. This exchange of position information between other entities (not only vehicles, but also other infrastructure nodes) is vital for actions such as cooperative maneuvers and to counter loss of satellite sight (e.g., when entering a tunnel). Taking these facts into consideration, it is therefore expected that in the mid to long-term, municipalities and highways will possess dedicated private 5G networks for V2X operations with the aim of offering a plethora of vehicular services, including positioning ones. Since the existent scientific literature lacks an integrated analysis of precise positioning services for ITS in 5G private networks, we propose in this paper, to provide a comprehensive review connecting these diverse elements, examining the role of 5G private networks in transmitting positioning messages in V2X scenarios. Additionally, the paper shall explore hybrid positioning systems that combine 5G and GNSS technologies, illustrating their potential to enhance V2X communications. This study offers a roadmap for the evolution of ITS and V2X communications by showcasing current trends and identifying areas for further research. 1. Introduction The automotive sector is undergoing a significant transformation driven by technological advancements in Intelligent Transportation Systems (ITS), that permit vehicles to support advanced actions such as cooperative maneuvers, platooning and lane merging, which are imperative in highly automated driving systems. The automotive industry is characterized by its stringent safety concerns, so having cars offering these advanced actions will required them to be capable of efficiently communicate and exchange data and other control information between themselves and the surrounding infrastructure and sensors. And by efficiently, this means rapidely, i.e., with extreme low ☆ This work is supported by the European Union / Next Generation EU, through Programa de Recuperaç˜ ao e Resiliˆ encia (PRR) [Project Nr. 29: Route 25 (02/C05i01.01/2022.PC645463824–00000063). * Corresponding author at: Capgemini, Rua Serpa Pinto, 44, Vila Nova de Gaia 4400-012, Portugal. E-mail address: [email protected] (B. Mendes). Contents lists available at ScienceDirect Vehicular Communications journal homepage: www.elsevier.com/locate/vehcom https://doi.org/10.1016/j.vehcom.2025.100878 Received 23 April 2024; Received in revised form 11 November 2024; Accepted 12 January 2025 Vehicular Communications 52 (2025) 100878 2 latency, and with high reliability, through a Vehicle-to-Everything (V2X) communication system. 5G networks offer promising support for V2X communication in this context. However, most studies focus on public 5G networks managed by Mobile Network Operators (MNO)s, leaving a gap in understanding how private 5G networks can address the specific demands of V2X systems, particularly for autonomous driving. Public 5G networks, while capable, often face challenges such as resource congestion and unpredictable latency due to their shared nature. And although 3GPP has pre-defined V2X network slices that can segregate resources, so they can be dedicated to vehicular communications, an MNO is unlikely to possess the necessary dedicated bandwidth that such a slice could potentialy require. This paper addresses this gap by focusing on private 5G networks and their potential to support cooperative localization, an essential requirement for V2X applications. Cooperative localization allows vehicles to share precise position data and surrounding infrastructure, enhancing situational awareness and improving safety. This process combines Global Navigation Satellite Systems (GNSS) data with relative 5G measurements to ensure accurate positioning, even in environments where GNSS is unreliable, such as urban canyons or tunnels. Moreover, it is important to mention that GNSS only offers highaccuracy location estimates when using correction services provided by a third-party (which can be public and free data, as Galileo offer this service). This correction data is typically send out by a satellite to be then processed in the car satellite receiver. This traditional solution has two problems: first the latency of the correction signal transited by the satellite; and second, car manufacturers avoid having devices embedded in the vehicle with high processing capabilities. Fortunately, 3GPP took this into consideration and current 5G architecture is designed to be able to send out post-processed GNSS correction data directly to the vehicle. Thus, by integrating real-time GNSS corrections with 5G-based positioning, private 5G networks can deliver the precision required for locationbased tasks like obstacle avoidance. This paper explores strategies for leveraging private 5G networks to overcome these challenges, providing a step closer to the realization of fully autonomous driving. As shown in Fig. 1, the 5G-V2X connected ecosystem contains several components such as GNSS satellites, a 5G communication tower, and autonomous vehicles. This configuration shows how private 5G networks can enable data exchange and improve localization accuracy for autonomous driving, particularly in complex urban environments. This survey investigates the necessary technologies to achieve this ecosystem. One of the significant challenges identified is the unreliability of public 5G networks for V2X communication. Public networks must accommodate a wide range of users, leading to resource contention and fluctuating latency, which can critically impact safety-related applications like cooperative driving. Timely delivery of Cooperative Awareness Messages (CAM) and Distributed Environment Notification Messages (DENM) is essential for road safety, yet public networks struggle to maintain the consistent performance required for real-time communication. In contrast, private 5G networks offer exclusive network capacity and guaranteed Quality of Service (QoS), explicitly tailored to V2X applications. This paper suggests that private 5G networks are better suited for V2X, and offer the flexibility to design customized communication infrastructures optimized for the specific needs of ITS. Another significant issue is positioning accuracy, a crucial factor for autonomous vehicles, which public 5G networks do not address sufficiently. While GNSS, such as GPS and Galileo, are the standards for vehicle positioning, their performance is often compromised in complex environments. Hybrid positioning techniques that integrate GNSS and 5G-based localization are proposed, combining terrestrial and nonterrestrial systems to provide sub-meter accuracy in critical vehicular applications. This hybrid approach holds significant potential to improve autonomous vehicle operations in challenging environments like tunnels or dense urban areas. The paper also examines how private 5G networks can address issues like packet delivery rate, bandwidth limitations, and latency, which are crucial for maintaining cooperative localization in V2X communication. Delays or packet loss can lead to vehicle miscommunication, increasing the risk of collisions or unsafe driving maneuvers. By providing guaranteed packet delivery and real-time GNSS corrections, private 5G networks can ensure continuous and reliable localization, even in scenarios where GNSS signals are weak or disrupted. This opens the door to ultra-reliable V2X communication, vital for deploying autonomous vehicles in complex environments. The instantaneous advancement of 5G technology has opened new possibilities for V2X communication, setting the stage for transformative improvements in vehicle safety, traffic efficiency, and autonomous driving systems. Current studies underscore the potential of 5G-enabled V2X systems across various use cases, yet there is a notable gap regarding high-precision localization, which is essential for real-time V2X scenarios. Foundational work, such as that by [1], thoroughly discusses V2X use cases and the architectural needs for 5G support but does not delve into the specialized requirements for accurate, reliable positioning. Similarly, [2] addresses the integration challenges within 5G V2X from a business and regulatory standpoint but offers limited insight into technical enhancements for localization. As V2X applications increasingly rely on precise and continuous location data, these broad perspectives highlight a clear need for research targeting the localization challenges inherent in V2X contexts. Technical surveys on 5G infrastructure and standards for V2X, like those by [3] (see Table 1), explore the landscape of 5G V2X technology comprehensively. Yet, they do not specifically address how dedicated network configurations (such as private 5G networks) might enhance V2X localization. While these studies establish an essential foundation for standardization and infrastructure, they leave open the question of whether more specialized network setups could support the precise positioning required for safety-critical V2X functions. Other studies on platoon cooperation in V2X, such as the work by [4], propose valuable solutions for intra-platoon communication through resource allocation Fig. 1. Illustration of a 5G-V2X ecosystem with GNSS satellites, 5G communication tower, and autonomous vehicles. B. Mendes et al. Vehicular Communications 52 (2025) 100878 3 and power management. However, these insights are limited primarily to platoon-based use cases, overlooking the broader potential for high-accuracy localization in varied and complex V2X scenarios that exist beyond structured platoons. Security remains a concern in 5G V2X research, as underscored by works like [5], emphasizing the importance of secure data exchange in V2X. Yet, these studies rarely discuss security integration within high-precision localization, where reliable positioning is crucial to the safety and success of V2X applications. In safety-critical contexts, the precision of localization must be paired with security to prevent disruptions or inaccuracies in the data on which these systems depend. Given these gaps, this research focuses on leveraging private 5G networks as a tailored solution for supporting high-precision localization in V2X environments. Unlike public 5G infrastructure, a private 5G network provides dedicated resource allocation, precise control, and low-latency communication, all critical for achieving the accuracy and responsiveness needed in real-time V2X localization. By optimizing network configurations specifically for these positioning needs, this study aims to enhance the reliability and security of V2X localization. This approach does not merely address the technical limitations seen in broader V2X frameworks and offers a practical, scalable foundation for the next generation of ITS. A literature review was conducted to ensure this research is grounded in the latest advancements and addresses critical gaps. The review targeted studies on deploying private 5G networks for V2X communication and hybrid positioning methods that integrate GNSS and 5G technologies. Databases such as IEEE Xplore, ACM Digital Library, and ScienceDirect were searched using carefully selected terms related to private 5G, V2X, hybrid positioning, and associated challenges like latency, interference reduction, and network reliability. Only peerreviewed studies with clear methodologies were included to maintain academic rigor. The review identified common themes, challenges, and solutions, particularly in how private 5G networks enhance cooperative localization, improve network reliability, and address positioning challenges in complex environments. It also highlighted the potential of hybrid GNSS-5G methods to overcome localization issues, especially in urban multipath scenarios. By synthesizing the existing literature, this review revealed critical gaps that this paper seeks to address, focusing on integrating private 5G networks with V2X communication and advanced positioning technologies for autonomous vehicles. As the V2X advances, supporting the demands of autonomous driving requires connectivity and exact and lowlatency communication. The 5G network includes potential congestion and fluctuating latency, which can compromise autonomous systems’ reliability and safety. Private 5G networks, on the other hand, emerge as a promising alternative by providing dedicated resources that better meet the exacting requirements of V2X applications. Their role in enhancing safety and reliability in V2X communication is reassuring for the future of autonomous driving. This document examines how private 5G networks can address the precise localization and communication requirements of V2X systems, especially in contexts demanding high accuracy and low latency. The introduction establishes the scope, highlighting the transformative potential of 5G advancements for autonomous driving and underscores the need for dedicated private networks to enhance V2X localization. The Chapter 2 explores V2X communications as a foundation for autonomous driving, focusing on the critical infrastructure required for various autonomy levels. It emphasizes the role of Cellular Vehicle-toEverything (C-V2X) and ITS in ensuring safe, coordinated vehicle operations. This foundational analysis frames the potential impact of private 5G networks on vehicle coordination and localization. Chapter 3 addresses V2X localization techniques, analyzing both 5G and GNSS methods. This chapter discusses the limitations of GNSS in urban environments and highlights the enhanced accuracy that private 5G can provide, especially for real-time V2X applications. Emphasizing precision, it argues for the distinct advantages of private 5G in achieving reliable, high-accuracy localization. Then Chapter 4 contrasts public and private 5G, focusing on the limitations of public networks, including resource contention and inconsistent latency, which hinder safetycritical V2X applications. This chapter suggests that private 5G with unlicensed spectrum can more reliably support realtime communication needs through dedicated, low-latency environments. Finally, Chapter 5 outlines research directions and proposes private 5G as a scalable infrastructure to address V2X’s evolving demands. Exploring specialized configurations positions private 5G networks as a promising path for future autonomous transportation systems, meeting the rigorous requirements of precision, security, and reliability. It explores the lessons learned throughout the research. 2. V2X communication and autonomous driving Autonomous vehicle operation depends on sophisticated communication networks that enable fast, reliable, and seamless data exchange with surrounding systems. V2X technology, mainly C-V2X, is instrumental in meeting these requirements across different levels of autonomy, from driver assistance to full automation, as defined by the Society of Automotive Engineers 1 (SAE). Autonomous driving refers to the ability of a vehicle to operate independently without the need for human intervention. It is a promising application driven by advancing technology, including Table 1 Summary of Existing 5G V2X Studies and Proposed Differentiation with Private Network for Localization. Study Title Focus Area Limitations Differentiation with Private Network for Localization Use Cases, Requirements, and Design Considerations for 5G V2X [1] V2X use cases, design for automated driving, architectural guidance Broad overview, lacks focus on precise localization Uses private 5G to enhance V2X localization precision, improving reliability and network control. A Business and Legislative Perspective of V2X and Mobility Applications in 5G Networks [2] Business models, legislative challenges in V2X for 5G Businessoriented, lacks tech-nical localization framework Highlights private 5G to pro-vide dedicated localization resources, ensuring better accuracy and latency. A Survey of 5G Technology Evolution, Standards, and Infrastructure Associated With Vehicle-toEverything Communications by Internet of Vehicles [3] Evolution and standards of 5Gfor V2X, technical issues Broad survey, limited on localization specifics Proposes private 5G for highprecision localization, offering capabilities not available in public networks. Platoon Cooperation in Cellu-lar V2X Networks for 5G and Beyond [4] Resource allocation and power managemen for platoon V2X Focuses on platoon use cases, lacks broader V2X localization support Extends private 5G for broader V2X localization in complex scenarios beyond platooning. Security of 5G-V2X: Technologies, Standardization, and Research Directions [5] Security in 5GV2X, focusing on reliability and latency High-level security approach, minimal focus on localization security Adds secure localization in private 5G for V2X, crucial for safety-sensitive applications. 1 https://www.sae.org/. B. Mendes et al. Vehicular Communications 52 (2025) 100878 4 artificial intelligence and advanced sensors [6]. Autonomous driving involves using complex systems such as sensors (e.g., cameras, radar, and lidar), data processing algorithms, and control systems to enable a vehicle to identify and respond to its surrounding environment. These systems collect real-time information about traffic, obstacles, pedestrians, and other road elements, allowing the vehicle to make informed decisions about acceleration, braking, changing lanes, and other maneuvers. Autonomous vehicles cannot rely only on their onboard sensors, and to operate effectively, they need robust infrastructure with advanced communication capabilities that support fast, reliable, and low-latency data exchanges with surrounding systems. V2X technology, particularly C-V2X, meets these demands by providing multiple modes of interaction that enhance safety, efficiency, and user experience across different levels of vehicle autonomy. With this in mind, Society of Automotive Engineers (SAE) defined different levels of autonomous driving, classified according to the degree of human intervention required and autonomy of a vehicle, known as Automation Levels, [7]. Here are the primary levels of automation: •Level 0 - No Automation: The driver controls the vehicle. There is no automation present. •Level 1 - Driver Assistance: The vehicle has adaptive cruise control (ACC) and steering assistance features. However, the driver retains complete vehicle control and must always be involved in driving. •Level 2 - Partial automation: The vehicle can control acceleration, braking, and steering in certain situations. However, the driver is still responsible for supervising the driving environment and must be prepared to regain vehicle control when necessary. •Level 3 - Conditional Automation: The vehicle can control driving under predefined conditions. The driver can disengage from driving tasks and perform other activities but must be prepared to resume control when the system requests. •Level 4 - High automation: The vehicle can be autonomous in almost all conditions but may require driver intervention in exceptional situations. The driver can choose not to be involved in driving in certain situations. •Level 5 - Full automation: The vehicle is fully autonomous and does not require human intervention. There is no need for a steering wheel, pedals, or any manual controls. In order to help the developments and architecture of these systems, the European Union (EU), legislation has created specific rules related to the automotive spectrum and applications that follow particular guidelines. In the context of connected vehicles and ITS, there are two distinct groups: critical and non-critical systems. The EU has defined specific spectrum bands for critical ITS systems. In the public spectrum band, the 2.6 GHz range is reserved for the use of it. This frequency band enables the implementation of C-V2X communication services for applications requiring fast response times and reliable communications in safetycritical situations. In addition, the EU has also designated the 5.9 GHz spectrum band for critical ITS communications on a private basis [8]. This study intends to showcase the usage of this spectrum band to send messages containing precise localization with the objective of a full collaboration to achieve level five of automation. 2.1. Cellular vehicle-to-everything (C-V2X) C-V2X is a sophisticated wireless communication technology that enables vehicles to interact with diverse objects and systems in their environment, such as Vehicle-toVehicle (V2V), Vehicle-toInfrastructure (V2I), Vehicle-toPedestrian (V2P), and Vehicle-toNetwork (V2N). It uses the same network infrastructure as smartphones and other cellular devices, enabling communication between cars and other road users, pedestrians, and infrastructure (such as traffic lights and road signs). Based on LTE and 5G cellular technologies, C-V2X utilizes the same network infrastructure as cellular devices, allowing vehicles to communicate with each other and external elements like traffic signals and road signs. Each mode of C-V2X communication supports specific applications. For instance, V2V enables essential data sharing for collision avoidance, V2I optimizes traffic flow, V2P enhances pedestrian safety, and V2N connects vehicles to the cloud for continuous updates, as shown in Fig. 2. These capabilities open new possibilities for C-V2X to enhance autonomous driving functionality, which requires real-time coordination with the environment. International standards such as ITS-G5, DSRC, and C-V2X have been established to support consistent and effective communication across diverse V2X applications. Each standard addresses specific V2X requirements, optimizing data exchanges to achieve high performance across latency, reliability, and interoperability metrics essential for autonomous driving. The C-V2X is a crucial technology for developing connected and autonomous cars. It can increase traffic flow efficiency, enhance the travel experience by enabling new services and apps, and boost road safety. In [9], it is shown that there are efforts by 3GPP to standardize the relation between the C-V2X communications and the autonomous driving application. The study aims to simplify the 3GPP documentation and its role in achieving Level-5 autonomous driving. The result from these standards will boost the original equipment manufacturers (OEMs) and regions such as the USA and EU, decided on 5G as the technology of choice for C-V2X. 3GPP’s technical specification groups (TSGs) provide relevant specifications and technologies for V2X services, though navigating through the numerous documents can be complex. Releases 14 and 15 introduced V2X enhancements in 4G, while 5 G’s Rel-16 extended architectural aspects and service-oriented functionalities for advanced V2X services. Rel-16 and Rel-17 focus on NR sidelink communication improvements, resource allocation, power efficiency, and new use-case scenarios. The evolution of C-V2X spans V2V standards, essential safety services, stable LTE-V2X in 4G, and advanced C-V2X services in 5G NR-V2X, delivering lower latency, ultra-reliability, and higher throughput. The depicted phases guide researchers in developing V2X solution components. In short, the authors examined the 3GPP’s support for V2X services in 4G, NSA-based 5G, and SAbased 5G, as well as the communication needs, use cases, significant problems, and potential solutions. Additional summarized information is provided in Fig. 3. The requirements set by 3GPP ensure that the communication system can effectively handle the demands of various applications. By addressing the performance requirements in Table 2, the communication system can effectively support advanced V2X scenarios for various use case groups, ranging from safety applications to traffic management, efficient navigation, and enhanced driver assistance. Real-world testing on the C-V2X network revealed the significance of specific configurations for achieving effective autonomous driving communication. In a study by [10], field trials conducted across Europe Fig. 2. C-V2X communications. B. Mendes et al. Vehicular Communications 52 (2025) 100878 5 and China examined the performance of 5G/LTE-V2X networks under different conditions, including variations in antenna height, driving speeds, and terrain. Vehicles equipped with onboard units were connected to a 5G or LTE infrastructure, making it possible to measure some factors such as latency, data throughput, packet loss, and connectivity impact. Packet loss rates were approximately 10%, which shows promise but also emphasizes the importance of optimal network configuration to meet the stringent requirements of autonomous driving. Moreover, in [11], the authors proposed a resource allocation method that addresses high-reliability constraints for multicasting automotive messages in 5G NR C-V2X networks. Based on a successive convex approximation method, the algorithm enhanced network performance under high computational demands. Tests showed improvements in reliability and latency, indicating the potential of resource management techniques to support autonomous driving requirements effectively. Adopting C-V2X with 5G NR technology is critical to achieve full automation. Testing and standardization progress will be essential to develop the low-latency, highreliability network required to support fully autonomous vehicles in the real world. This creates the basis to achieve the connectivity needed to exchange information between all the components of a cooperative infrastructure. 2.2. Intelligent transport systems (ITS) ITS transform transportation by combining cutting-edge technology, communication networks, and information processing to improve productivity, security, and sustainability. These systems comprise various components that work in unison to streamline traffic networks and offer authorities and users real-time information [12]. Traffic management systems use sensors, cameras, and data analytics to monitor and control traffic flow. Techniques include adaptive traffic signal control, incident detection and management, and congestion pricing. Traveler information systems offer real-time updates on traffic conditions, travel times, detour options, and public transportation schedules. To achieve a cooperative approach, ITS can send a set of possible messages across the network, categorized into several services. V2I Safety messages include curve speed advice, stop sign gap assistance, and red-light violation warnings. V2P safety messages, meanwhile, help secure signalized crosswalks by notifying approaching vehicles. V2V safety services include alerts about hazardous passing situations, forward collision warnings, blind spot detection, and emergency electronic brake lights. The Environment category prioritizes eco-friendly driving by synchronizing speeds to maximize fuel efficiency, allowing cooperative adaptive cruise control, and optimizing traffic signal timing. Lastly, road weather services provide real-time data and alerts to help vehicles navigate hazardous conditions safely. A framework for ITS developed by the European Telecommunications Standardization Institute (ETSI) is shown in Fig. 4. This framework offers a modular communication system that can be customized for various traffic control applications and is compatible with different communication technologies [13]. Three major players in an ITS scenario are vehicles, roadside infrastructure, and a centralized management system. Each represents a unique subsystem. In the figure’s lower blocks, the OSI protocol stack divides communication responsibilities across layers: •Access (Layers 1 and 2) handles physical data transmission. •Networking and Transport (Layers 3 and 4) manage data routing and delivery. The “Applications” component contains ITS apps that connect using these services, working together to provide comprehensive ITS solutions. The ITS system employs a layered design for efficient communication. The access layer uses protocols like WIMAX and General Packet Radio Service (GPRS) to manage physical data transmission over wireless and wired mediums [15]. It supports both internal device commuFig. 3. 3GPP Roadmap towards full 5G NR C-V2X. Table 2 Performance Requirements to Support Advanced V2X Scenarios for Use Case Groups. Group End-to-end la-tency (ms) Payload (Bytes) Message/ Sec Data rate (Mbps) Min range (m) Reliability (%) Vehicle platooning 10–25 50–6500 2–50 50–65 80–350 90–99.99 Remote driving 5– – UL:25 DL:1 –99.999 Extended sensors 10–100 1600 10 10–90 50–1000 90–99.999 Advanced driving 3–10 2000–12,000 10–100 30–53 360–700 99.999 B. Mendes et al. Vehicular Communications 52 (2025) 100878 6 nication and external connectivity to other stations. The network and transport layers use protocols like IPv6 to ensure effective data delivery, handle congestion, and maintain message reliability. The facilities layer includes functions designed for ITS applications, keeping data storage and communication addressing with message processing protocols, while allowing devices to download additional software modules for enhanced functionality. This information is essential to maintaining a cooperative system and is distributed through smartphone applications, moving message signs, and in-car navigation systems. In [16], a study investigated a system to collect, deliver, and display road weather data to improve driving under adverse weather conditions and enhance traffic safety. The study involved a 260km route, six trucks equipped to collect data, and real-time weather forecasts. The ITS technology enhances system efficiency and reliability when applied to public transportation systems. Real-time tracking, automatic fare collection, and transit signal priority improve customer experience. Similarly, ITS enables the quick deployment of electronic toll collection, eliminating the need for physical toll booths and easing traffic congestion. This is achieved through RFID and ITS messaging. Incident management systems improve the detection and handling of road incidents, enabling quicker coordination among emergency services, traffic authorities, and road users. In Smart Cities, ITS facilitates seamless connectivity for autonomous vehicles, public transportation, and infrastructure, transforming vehicles into real-time information sources for traffic and resource management [17]. Technologies like autonomous driving, advanced driver-assistance systems (Advanced driver-assistance system (ADAS)), V2X, V2V, and V2I communication enable cars to interact with one another and the surrounding infrastructure [18]. In Europe, ITS plays a critical role in reducing pollution and congestion while enhancing safety. ICT technologies encourage secure, effective, and environmentally friendly transportation across all modes. The EC collaborates with Member States, businesses, and governmental agencies to overcome deployment challenges and ensure coherent implementation. Studies like [19] discuss ITS contributions to energy conservation and emission reduction, identifying how optimal vehicle specifications and government-backed energy-saving policies can further these efforts, particularly in Smart Cities. Rapid advances in ITS are expected in the coming years, driven by cooperative ITS (Cooperative-Intelligent Transport Systems (C-ITS)) and automation, which enable efficient data exchange and communication between vehicles, infrastructure, and users. The C-ITS system helps create a cleaner, safer, and more efficient transportation system by reducing environmental impact, maximizing infrastructure use, and improving safety. The EC aims to leverage ITS systems to enhance transportation network management and optimize passenger and business operations [20]. Portugal’s adoption of ITS Law No. 32/2013, which recognizes ITS in road transport, reflects this trend, aligning with Directive No. 2010/40/EU [21]. The costs associated with traffic congestion are significant, estimated at € 100 billion in 2012 [22], but implementing C-ITS services brings benefits that outweigh these costs [23]. C-ITS can employ both direct short-range connections (V2V) and cellular networks (3G, 4G, 5G), as shown in Fig. 5, to allow effective communication across multiple levels of automation. Studies like [24] demonstrate ITS applications in pedestrian-to-vehicle (P2V) communication, proposing a framework where smartphones and vehicular communication systems enhance pedestrian safety. Similarly, study [25] focuses on collision prevention between cars and bicycles using low-cost C-ITS stations. Such initiatives demonstrate the critical role of C-ITS in improving road safety and efficiency. ITS services rely on accessible radio communication technologies. Dedicated Short Range Communications (DSRC) and V2X operate in designated frequency bands (e.g., 700 MHz and 5.9 GHz) for critical applications like automated driving. Table 3 summarizes the characteristics of these technologies by service type. For example, electronic toll collection uses DSRC for bidirectional data over short distances, while V2X supports vehicle and road safety applications at longer ranges. Safety is a priority in these services. Secure communication is critical for DSRC and cellular-based C-V2X technology, especially for ADAS and automated driving. However, achieving interoperability between ITS-G5 and C-V2X standards involves technical and regulatory challenges, particularly spectrum allocation and backward compatibility across regions [26]. Ensuring seamless integration of these standards remains a primary focus for organizations like ETSI and 3rd Generation Partnership Project (3GPP) [27]. In the coming years, integrating ITS with advanced technologies like 5G and networked remote units (New Radio Unlicensed (NR-U)) will expand the system’s range, allowing for extensive infrastructure communication beyond the current OSI model. These advancements enhance interoperability and address security, latency, and scalability challenges. 2.2.1. Standards and interoperability: ITS-G5, DSRC, and C-V2X The terms DSRC and ITS-G5 are often used interchangeably to refer to the same communication technology, with DSRC more commonly used in North America and ITSG5 in Europe [28]. Both technologies play essential roles in ITS, enabling communication between vehicles (V2V) and infrastructure (V2I), to support cooperative and safer Fig. 4. ETSI ITS Communication Architecture [14]. Fig. 5. ETSI C-ITS Communications. B. Mendes et al. Vehicular Communications 52 (2025) 100878 7 transportation systems. Essential standards that govern ITSG5 or DSRC communication include ETSI EN 302 663 and IEEE 802.11p for vehicular communication protocols, EN 302 571 for security services, and EN 302 637, which outlines certificate management and security components [29–31]. Despite regional nomenclature variations, the fundamental concepts and objectives of V2V and V2I communication are consistent, ensuring interoperability across platforms for secure and efficient transportation. A comparative study of vehicular communication systems on unlicensed versus licensed DSRC bands showed that performance was comparable in the 2.4–2.5 GHz unlicensed frequency range, although there was a 30% drop in packet delivery rate on the 5.725–5.875 GHz unlicensed band [27]. This highlights the potential interference challenges on unlicensed frequencies, underscoring the need for further research into the performance and coexistence of vehicular systems on non-regulated frequencies (NR-U). Beyond DSRC and ITS-G5, C-V2X is a communication technology that supports V2N and V2V modes through 3GPP standards, namely LTE-V2X and 5G NR-V2X [32]. LTE-V2X leverages existing LTE networks, while 5G NRV2X operates within 5G environments, providing advanced features such as low-latency communication and support for massive machine-type communication essential for highly dense traffic and automation. While C-V2X and DSRC/IST-G5 differ in underlying communication methods and network dependencies, they both support crucial ITS applications. However, integrating these technologies remains an industrywide challenge, with standardization bodies working toward effective interoperability across regions and systems. To facilitate interoperability between ITS-G5 and C-V2X, several strategies have been proposed, including organizing communication using superframes with structured time slots, message headers, and reservation signals to manage occupied slots effectively [33]. These measures are designed to support compatibility between vehicles equipped with different communication technologies, thus enhancing overall system efficiency and road safety. A real-world performance assessment compared DSRC/ ITS-G5 and C-V2X under highway conditions. Results indicated that for shortrange applications, C-V2X using PC5 typically achieved a higher range, while ITS-G5 provided lower latency in low-traffic density environments [26]. The study suggests that 4G C-V2X could potentially substitute some use cases that require long-range communication. However, the advent of 5G C-V2X might further enhance range, latency, and scalability. Future research could explore the capabilities of 5G C-V2X in real-world deployments to evaluate its impact on communication reliability and efficiency, particularly within NR-U spectrum. As discussed, precise and reliable localization is essential in autonomous driving. C-V2X technology enhances the vehicle’s capacity to navigate safely and effectively in dynamic environments. However, its effectiveness largely depends on the types of messages transmitted within the system, as messages with accurate localization are essential to achieve new levels of automation. Therefore, ensuring precise localization and encapsulating this data efficiently within these systems is fundamental. All this showcases the importance of advanced localization techniques in autonomous driving, demanding a focused exploration of localization and the solutions available to support it. While GNSS provides a foundational layer for positioning, its limitations in urban and complex environments require additional methods. Therefore, advanced localization techniques, including relative positioning and hybrid GNSS-5G solutions, are increasingly essential to achieve the sub-meter accuracy demanded by autonomous vehicles. The next chapter explores these localization methodologies, emphasizing the integration of GNSS with 5G technologies. It will highlight how different localisation methods can significantly enhance precision, establishing them as indispensable elements of the autonomous driving ecosystem. 3. Localization in V2X: leveraging 5G and gnss technologies As autonomous and connected vehicle ecosystems develop, reliable network-based positioning has become an objective towards safe and efficient operations. Precise localization enables V2X systems to coordinate maneuvers and improve situational awareness. Given the limitations of standalone GNSS in complex urban environments, combining GNSS with 5G positioning offers an effective approach to achieve high precision and higher speeds in first position retrieval, required for autonomous systems. This section discusses fundamental localization techniques, including GNSS, 5G-based positioning, and hybrid methods. When applied to ITS, these methods enable accuracy and efficiency to support different scenarios. 3.1. Positioning technologies The 5G Core Network (CN) serves as the central component of the 5G network architecture and contains several interconnected elements designed to provide various services. One is the location-based functionalities for applications such as emergency services, asset monitoring, and location-based marketing [34]. In particular, theLocation Management Function (LMF), introduced in Release 16 of the 3GPP standard [35], plays a crucial role in the 5G CN, explicitly enabling the location of mobile terminals and other connected devices. This addition enhances the network’s capability to support advanced positioning applications essential for user safety and operational efficiency. These services have dedicated regulations. [36] and [37] state minimum regulatory positioning target values. The maximum target is 50 m for 80% of UE for Horizontal positioning error, 5 m for 80% of UEs for vertical positioning error, and an end-to-end latency of 30 seconds. 5G evolves 4G’s positioning architecture with adjustments for new components in the former’s Core Network [38]. These leverage the geolocation capabilities of mobile devices to provide specific information and features based on the user’s location [39]. With LTE, service providers can use the accuracy of the GPS built into mobile devices to offer various location-based services. However, 5G offers better characteristics when compared with LTE. 5G goes beyond its exclusive focus on location-based services, bringing sub-stantial advancements to this domain. The Next Generation Radio Access Network (NG-RAN) facilitates 5G positioning through a structured set of signalling protocols and interactions between network components, as illustrated in Fig. 6 and described in [40]. Within NG-RAN’s functional framework, the gNB is divided into a Central Unit (CU) and a Distributed Unit (DU), which communicate via the F1 interface (1). To support accurate positioning, Table 3 Characteristics of radio technologies per ITS service. Service types Radio communication technologies Information Radio Coverage Message Latency ETC DSRC Bi-directional data Small (~100 m) Low (<100 ms) Vehicle and Road Safety 700 MHz or 5.9 GHz band V2X Bi-directional data, Data Broadcasting Medium (~1000 m) Low (<100 ms) Traffic Information Service TPEG Data Broadcasting Wide (~100 km) Medium (~1 s) Autonomous Driv-ing V2X Bi-directional data, Data Broadcasting Medium (~1000 m) Low (<5 ms) B. Mendes et al. Vehicular Communications 52 (2025) 100878 8 the New Radio Positioning Protocol Annex (NRPPa) (2) enables data and positioning observations to be shared between the gNB and the LMF (3) [41]. Additionally, NG-RAN transmits essential radio setup data to the UE through the NR-Uu interface, part of the Radio Resource Control (RRC) layer (4,5). For 5G positioning services between the UE and LMF, the LTE Positioning Protocol (LPP) has been extended to support both 4G and 5G advancements within a unified framework (6). This integration is essential for applications requiring precise and rapid positioning, such as V2V communication and precision industrial automation, where 5G’s capabilities offer substantial advantages. Moreover, advanced antenna technologies, including beamforming and Massive MIMO, further enhance 5G network performance by improving efficiency, coverage, and accuracy in position calculations. One way to add location management capabilities to the 5G CN is to use Location Services (LCS) built into it. LCS, mentioned on the state of the art [42], can locate devices using various location technologies such as Global Positioning Systems (GPS) and Cell ID, and it can be used for several use cases like vehicular localization, IoT and emergency scenarios, as reported in [43,44]. It will determine the final location provided by the network location algorithm and estimate the speed and accuracy of its location. Although some 4G components already perform some of these operations, 5G allows greater accuracy and a significantly higher number of operations without impacting the state of the network. Another option is to use a positioning protocol such as the 5G Positioning Protocol, an extension of the LTE Positioning Protocol (LPP), designed to work with the 5G network architecture [37]. 5GPPP in [45] provides a standard positioning method that can be integrated into the 5G CN. It is also responsible for choosing the method to calculate the location based on the client’s location (i.e., urban or non-urban), required accuracy, and latency. This feature also makes it possible to combine information collected from different sources, i.e., a hybrid location service that can use the results of different algorithms and provide position estimates from all algorithms. One of the main advantages of the 5GPPP is its capacity to deliver exact location data, even when conventional positioning technologies, like GNSS, may not be accessible or efficient. The 5G architecture, in service-based architecture (SBA) standardized by 3GPP, makes traditional network structures obsolete as they are divided into modular and interconnected services [34]. Each service embodies a specific business function to promote decentralized and agile network environments. These services communicate through distinct interfaces commonly used by APIs, service discovery, and registries concerning efficient communication within the SBA framework. Besides, a service mesh assists interservice communication, offering functionality such as traffic management, security enforcement, and observability. Rest protocol-based external connectivity towards SBI in the context of SBA has made the concept of SBA necessary for the use of prescribed communications protocols. To facilitate smooth component-tocomponent communication via the SBI, protocols such as the NonAccess-Stratum (NAS) and Next-Generation Application Protocol (NGAP) [46,47] are essential for controlling registration, connectivity, resource allocation, mobility, and context transmission within the 5G network. The network architecture must be carefully considered to implement LMF functionality inside the 5G CN, with the SBI as the conduit linking all network components [42]. As seen in Fig. 7, LMF communicates with other network functions by sending HTTP POST requests using the Nlmf interface in a RESTful API manner. This integration emphasizes how Fig. 6. Existing protocols on data exchange between LMF, AMF, gNB(CU/DU) and UE. B. Mendes et al. Vehicular Communications 52 (2025) 100878 9 crucial the SBI is to enabling harmonious and effective functioning in the context of 5G Another critical 5G core function is the Access and Mobility Management Function (AMF), which uses the NAS protocol to record administration and connections, handle system identification and authorizations, and ultimately control network movement. To connect the LMF to the 5G core, an interface called Namf is required. This interface connects to the SBI and enables the usage of the function [34]. This interaction between the AMF and the LMF is needed to enable LBS for the users, and it will be responsible for managing the mobility and interacting with the LMF for managing the location information. Although the LMF is optional, it can and should be implemented in a 5G system to enhance location management capabilities. While the AMF is responsible for some location functionality, the LMF brings added value. It will allow additional flexibility regarding algorithms, permitting customization and optimization in specific use cases. It also increases accuracy by working with the AMF and integrating various location data sources, such as GPS and Wi-Fi. It allows a broader range of services, such as geographic data analysis, that require higher resources. While the AMF is essential, the LMF is desirable, especially when moving forward with autonomous cars, where it can become an indispensable asset. Fig. 7. 5G Architecture with Core Component, LMF and RAN. Fig. 8. Diagram supporting the communication between all positioning components (adapted from [48]). B. Mendes et al. Vehicular Communications 52 (2025) 100878 16 satellites and two receivers, e.g., two vehicles, as a specific approach. This method could be more complex but offers higher precision as it eliminates both satellite and receiver clock errors. In the study, employing DDT would involve comparing signals from multiple satellites, such as the moving vehicle’s receivers and a stationary reference. (ex. gNodeB). This approach is particularly beneficial in dynamic environments, as it significantly improves the reliability and accuracy of attitude determination by minimizing various error sources. [83] introduces a DDT and dual-frequency model for exact positioning and attitude determination. This is particularly effective for observable GNSS signal resources, including satellites and transceivers, and this kind of approach could be significantly relevant for the Internet of Vehicles (IoV) applications, offering a low-cost, high-reliability, easily installed, and conveniently maintained solution. The integration of GNSS and pseudo-lite technology for enhanced positioning in urban areas and various other applications is discussed in the paper, which addresses technical challenges such as near-far signal strength, multipath errors, and time synchronization issues in GNSS networks. The results demonstrate high accuracy in positioning and attitude determination. For example, the measured horizontal direction error was less than 6 mm, and the vertical direction error was less than 15 mm. The system also effectively determined vehicle attitudes like head, pitch, and roll angles with minimal error. In another case, [84] presents a comprehensive analysis of a GNSS spoofing detection technique based on double difference scattering. In this consideration, a differential carrier phase model involves two receivers observing identical satellites. This model calculates single carrier phase differences for each satellite in a standard view, considering factors such as geometric range, integer carrier phase ambiguity terms, receiver clock error, and differential noise. The algorithm proposed in this study uses the signal AoA. Under normal conditions, GNSS signals from different satellites arrive at the receiver from various directions. In contrast, spoofed signals from a single source have a typical AoA. The algorithm differentiates between authentic and fake signals based on this principle. However, a third non-collinear antenna is required to resolve ambiguities inherent in a two-antenna system. In this case, the overall performance of the detection algorithm depends on the combination of pairwise detections. The overall probability of missed detection (PMD) is calculated based on the probability of detection for various combinations of DDT measurements. To increase detection robustness against noise, the paper suggests averaging fractional DDs over short intervals before deciding. This reduces noise variation and consequently affects the detection limit and the probabilities of missed detection and false alarms. Furthermore, using two baselines (i.e., adding a third antenna) allows simultaneous detection of spoofed signals on both baselines, increasing detection reliability. 3.2.3. Kalman filter for accurate positioning The Kalman Filter [85] is a fundamental algorithm in control systems and signal processing, offering a robust solution for estimating the state of dynamic systems with different noisy measurements. Its iterative nature and computational efficiency are practical in precision-dependent, real-time applications like navigation systems. The Kalman Filter enhances accuracy in GNSS positioning by addressing inherent challenges such as signal attenuation and satellite misalignment. This capability is especially relevant in PPP applications, where accuracy is critical and traditional GNSS methods may face limitations like signal degradation. Thus, it can play a crucial role in increasing the positioning accuracy of the GNSS. This algorithm effectively addresses challenges inherent in GNSS, such as signal attenuation and satellite misalignment, especially in PPP method applications. By employing a recursive and linear approach, the Kalman Filter effectively integrates the dynamic characteristics of the model and observational data, thus enabling a more accurate estimation of the dynamic characteristics of a mobile operator. This is particularly beneficial in realtime applications such as autonomous vehicles in obstructed spaces, where traditional navigation measurements are prone to contamination and inaccuracies [86,87]. One example is implementing Kalman Filtering in GNSS, and Inertial Navigation Systems (INS) integration further underscores its significance. An integrated navigation system can be achieved by using the difference in position and velocity as measured by GNSS and INS, providing more reliable positioning information. As applied in this context, the discrete Kalman filter algorithm estimates the state for the next moment based on the current system state, thus enhancing the overall navigation accuracy and reliability [88]. Furthermore, the Kalman Filter is proficient in fusing data from multiple sensors to improve positioning accuracy. Autonomous vehicles rely on various sensors, such as GPS, LiDAR, radar, and cameras, to perceive their environment. Each sensor, however, has its limitations and inaccuracies. This is done by weighting inputs based on their respective uncertainties, effectively filtering out noise and reducing the impact of single-sensor errors. This results in a more accurate understanding of the vehicle’s condition, crucial for safe navigation and decision-making in complex and dynamic environments [89]. 3.2.4. GNSS and 5G integration: LMF interactions The LMF supports positioning techniques, including PPP, RTK, and PPP-RTK. Its primary function is to provide valuable assistance data to the UE to enhance the accuracy of its positioning capabilities [36]. With PPP, the LMF supplies essential assistance data to the UE. This data includes precise satellite orbits, clock corrections, code biases, and ephemeris and clock data if the UE has not acquired this information independently. The PPP technique achieves accurate single-station positioning by utilizing global networks of reference stations and atmospheric models. In the case of RTK, the LMF sends realtime corrections directly to the UE. These corrections are instrumental in improving positioning accuracy by eliminating errors common to the reference station and the UE. However, RTK requires the UE to be close to the reference station to achieve highly accurate positioning results. As done in PPP, the LMF interaction with PPP-RTK also provides real-time corrections, as in RTK, along with precise satellite orbit, clock data, and code biases obtained from reference stations. This combination enables PPP-RTK to achieve exceptional positioning accuracy even in regions beyond the reach of traditional RTK methods. According to [90], in exploring advanced positioning techniques, the integration of PPP-RTK emerges as a promising approach. The LMF plays a crucial role in this integration by providing essential assistance data to the UE. In PPP, the LMF supplies data such as precise satellite orbits, clock corrections, and code biases, utilizing global reference networks and atmospheric models for accurate positioning. RTK, on the other hand, relies on the LMF for real-time corrections to reduce errors common between the UE and the reference station. However, RTK’s effectiveness is limited by the proximity of the UE to the reference station. PPP-RTK overcomes this limitation by combining real-time corrections (as in RTK) with precise satellite data and code biases (as in PPP), enabling exceptional accuracy even in regions where traditional RTK is less effective. In summary, the LMF plays a critical role in facilitating the exchange of assistance data between the UE and various positioning methods (PPP, RTK, and PPP-RTK), ultimately leading to more accurate positioning information for the UE. Depending on the positioning technique and the UE’s requirements, the LMF can provide real-time or unsolicited periodic assistance data. Additionally, the UE can send GNSS measurements and other non-GNSS data to the LMF for location calculation or verification purposes. The 5G gnB links the 5G Vehicle Platform and the core network. Over this link, the vehicle transmits location or measurement data back to the LMF, which provides corrections and assistance data. B. Mendes et al. Vehicular Communications 52 (2025) 100878 17 3.3. Hybrid positioning: GNSS and 5G integration Hybrid positioning enables more precise and reliable location determination by combining multiple positioning methods, each compensating for the other’s limitations. This approach leverages the strengths of different technologies, such as GNSS and cellular networkbased positioning, to provide robust and adaptable location solutions. Hybrid positioning typically integrates GNSS with cellularbased positioning methods. While GNSS excels in open environments, it struggles in urban or indoor settings due to signal degradation or blockage. Cellular-based positioning in hybrid systems effectively addresses these challenges and helps decrease the time needed to find the first position, ensuring consistent and accurate location information across diverse environments. Hybrid positioning systems intelligently incorporate data from these several sources, for instance, a hybrid system may place users in urban areas using cellular network-based locations while using GNSS for outdoor placement. The system may offer a smooth and reliable positioning experience in various settings by combining the data from these sources. Applications for hybrid positioning include navigation systems, location-based services, asset tracking, augmented reality, and autonomous cars, all requiring precise and dependable placement. As can be analyzed in Fig. 1, locating the object using both technologies in the same algorithm is possible. 5G includes many capabilities that may help to increase the efficiency and accuracy of location [91]. The advantages of 5G for hybrid positioning are as follows: •Enhanced Network-based Positioning: Greater location precision is offered by 5G networks than by earlier ones. With the advent of 5G, network operators can provide more accurate location data thanks to improved signal processing methods and better network infrastructure density. It will permit more precise triangulation from cell towers and improved localization in populated regions. •Multi-Connectivity: 5G devices can connect to 5G and GNSS networks simultaneously. Hybrid positioning systems may use many positioning methods simultaneously because of their multi-connectivity capability. The total precision and dependability of location determination may be significantly enhanced by combining data from many sources, such as 5G networkbased positioning, GNSS. •Low-Latency Communications: Data transfer between the device and the network is quick because of 5G ultra-low latency. This reduced latency is especially useful for real-time positioning applications that demand quick updates. For instance, 5G low-latency transmission provides prompt and accurate position updates in autonomous cars and drones, promoting safer and more effective navigation. In [92], ITS and V2X communications, the hybrid positioning approach combining 5G and GNSS technologies presents an innovative solution for obtaining high-precision location data essential for the advanced functionalities of autonomous vehicles. Integrating the URLLC and highbandwidth capabilities of 5G with the broad spatial coverage of GNSS addresses the inherent limitations of each technology when used independently. 5G networks, with their dense infrastructure and edge computing capabilities, offer significant improvements in urban and indoor environments where GNSS signals are often weak or obstructed. This synergistic approach ensures continuous and accurate positioning information, even in challenging scenarios such as urban canyons or covered areas, where 5G or GNSS alone may fail. Furthermore, using 5G for real-time data transmission and processing allows rapidly disseminating GNSS correction data, further increasing positioning accuracy. This hybrid model elevates the reliability and accuracy of location-based services in ITS and makes way for innovative applications in traffic management, emergency response, and vehicle infrastructure interactions, strengthening the foundation of a truly interconnected and intelligent network. Finally, in [93], it is an expanding field in response to growing demands for accurate location tracking in diverse environments. The paper presents an innovative approach by integrating doubly differentiated 5G technology with undifferentiated and doubly differentiated GPS observations. This integration aims to overcome the limitations inherent in traditional GNSS, particularly their ineffectiveness in complex indoor environments due to signal attenuation and obstruction. The algorithm proposed addresses a significant challenge in 5G technology: clock synchronization errors at base stations, which can negatively impact positioning accuracy. By incorporating reference terminals, this algorithm effectively minimizes these errors, improving the accuracy of positioning data between terminals and base stations and even between the base stations themselves. The comprehensive experiments cover various GPS positioning models such as SPP, PPP, RTD, and RTK. The study meticulously compares these models in different scenarios with traditional GPS-only systems. The results are convincing; the integrated system consistently outperforms the GPS-only configuration regarding accuracy, stability, and convergence speed. This is particularly notable in environments with physical obstructions, where GPS systems typically struggle. The document highlights the potential of 5G in improving GNSS performance and the importance of adapting to the growing demands of outdoor navigation. The findings contribute significantly to the field of positioning technology, suggesting that the fusion of 5G and GPS could be the key to unlocking more reliable and accurate navigation solutions. A multiple-rate adaptive Kalman filter (MRAKF) for GNSS-5G hybrid location is introduced in [94]. By tackling issues with multi-rate fusion and the dynamic range of 5G measurement noise uncertainty, the goal is to improve positioning accuracy. The MRAKF algorithm adaptively modifies the observation noise covariance while allowing the fusion of GNSS and 5G measurements at various data speeds using a distance-dependent model. Additionally, a switchover technique is included to modify the placement mode according to the accessibility of base stations with the line of sight. The study assesses the proposed approach using simulations and an actual driving test. The extended Kalman filter (EKF) and four additional adaptive noise estimation techniques are compared in the simulations together with the performance of the MRAKF approach. The outcomes show that the MRAKF approach offers low computational cost, resilience in mixed LOS/NLOS settings, and a considerable improvement in positioning accuracy. The actual driving test further proves the method’s success in enhancing both vertical and horizontal positioning accuracy. In conclusion, the multi-rate fusion and 5G measurement noise uncertainty issues with GNSS-5G hybrid positioning are satisfactorily addressed by the suggested MRAKF approach. Regarding positioning precision, resilience, and real-time capabilities, it exceeds conventional EKF and alternative adaptive noise estimating techniques. This study recommends future research and advances the development of practical GNSS-5G fusion positioning algorithms. The work enabled by [95] aims to integrate 5G cellular positioning with GNSS to enable high-precision positioning for applications like driverless cars. The authors suggest a condensed method termed physical-layer abstraction that makes it simpler to simulate GNSS and 5G range observables at the system level. Using simulations, the researchers assess the positioning capabilities of GNSS and 5G downlink time-difference of arrival (DL-TDoA) in urban macrocell settings. The study aims to evaluate the positioning capacities of GNSS and 5G DL-TDoA in urban settings and determine the prerequisites for accurate positioning. The authors suggest a physical-layer abstraction approach, modeling GNSS faults with a Gaussian distribution considering the User Equivalent Range Error amount. They use a twostep process to estimate range errors for 5G observables. The receiving 5G signals were simulated to do so, and the cumulative density function (CDF) of errors per signal-tonoise level was interpolated. The simulation findings show that adopting hybrid systems, which incorporate several GNSS constellations and 5G DL-TDoA with a 100-MHz bandwidth, may achieve a B. Mendes et al. Vehicular Communications 52 (2025) 100878 18 horizontal positioning accuracy below 5 m in outdoor urban contexts for 95% of situations. Based on their findings, the authors conclude that the hybridization of GNSS and 5G is crucial for meeting the high-accuracy positioning requirements in urban environments. The proposed physical-layer abstraction method simplifies system-level simulations and enables the assessment of positioning capabilities. However, further research is needed to target even higher accuracy levels, such as submeter accuracy in 99% of cases. [96] aims to evaluate the performance of a hybrid positioning solution that combines 5G cellular technologies with GNSS in urban environments. The suggested threedimensional city map simulation approach determines the line-of-sight conditions between receivers and transmitters. The study evaluates the possibilities of a hybrid 5G RTT and multi-constellation GNSS solution in a steep urban canyon. The simulation findings show that standalone GNSS solutions are greatly enhanced when a single 5G RTT measurement is combined with GNSS observables. The hybrid positioning method consistently achieves less than 10 meters of horizontal accuracy in dense metropolitan settings. In conclusion, the simulation employing 3D city maps to develop the proposed hybrid 5G and GNSS positioning system shows promise in improving positioning availability and accuracy in urban settings. Standalone GNSS location constraints are solved via 5G RTT and GNSS observables. The research in [97] suggests a hybrid measurement strategy to handle the localization issues in a 5G-enabled GNSS. The goal is to determine if adding 5G observations to GNSS measurements can improve localization performance, particularly in areas without satellites. The proposed technique incorporates GNSS pseudo-ranges, GNSS carrier phases, 5G angle-of-departures, and 5G channel delays to estimate the user’s position. The three key processes are getting the float solution, dealing with ambiguities, and getting the fixed solution. The technique uses a variety of metrics to increase location estimate accuracy. Several simulations were run to assess how well the suggested method works. These simulations evaluate the carrier-phase ambiguity resolution success rate and the estimated inaccuracy of the user’s position when 5G observations are included. The findings show that using 5G observations makes localization possible even in situations with few visible satellites. The suggested approach significantly lowers estimate error and raises the success rate of carrier-phase ambiguity resolution. It works exceptionally well in areas with little satellite coverage when just a few satellites are visible. In order to increase accuracy and availability in GNSS environments with obstructions, [98] investigates the hybridization of GNSS with 5G positioning. The goal is to use the precision and accessibility of 5G signals under challenging situations, including urban canyons, to get around the constraints of standalone GNSS. The suggested method incorporates GNSS receivers receiving assistance from 5G mmWave communication technologies. In the study, the parameterization of signals from GNSS satellites and 5G base stations is discussed, and based on these parameters, get the information on location and velocity. In a case study, GNSS and 5G location hybridization for autonomous vehicles (AVs) are discussed in conditions with a low geometric dilution of precision (GDOP) and restricted GNSS visibility. Simulations are run to assess the efficacy of the hybrid system in scenarios including urban streets with subpar GNSS geometry. The simulations adjust for visibility restrictions in the scenario by considering the availability of GNSS satellites and 5G base stations. The simulation findings show that in urban street situations with subpar GNSS geometry, the hybridization of GNSS with 5G positioning significantly increases positioning availability and accuracy. The total performance is improved by minimizing the shortcomings of solo GNSS by adding 5G signals as a backup system or filling reception gaps. The article concludes that a viable solution to positioning issues in GNSS environments with obstructions is the hybridization of GNSS and 5G positioning. The findings suggest that integrating GNSS and 5G is a solution, especially for systems incorporating autonomous cars. [99] is another work that uses 5G small cells to enhance accurate location under challenging situations. The suggested approach combines base stations at the positional level and applies a Kalman filter (KF) with a dynamically tuned covariance matrix (DTCM) for improved sensor fusion. The methodology considers the geometry and connectivity of the BSs to solve linearization flaws of conventional extended Kalman filter (EKF) approaches. A case study was done in Toronto, Canada, to assess the effectiveness of the suggested strategy. The setup used in the case study aims to replicate real-world conditions to a certain extent. Compared to GPS PPP, simulations in a 5G simulator based on ray tracing evaluate the method’s accuracy in urban canyons. The outcomes demonstrate that the suggested technique outperforms GPS PPP and EKF-based fusion methods in terms of positioning accuracy. The RMS, maximum, and 95% errors show sub-meter accuracy. In conclusion, the positioning accuracy under challenging settings is greatly improved by incorporating 5G small cells at the positioning level with a dynamically modified covariance matrix in the Kalman filter. According to this study, GNSS-based positioning may one day be replaced by 5G technology for precision applications. The summarized result of this vast research can be observed in Table 9. In summary, the discussion of autonomous driving and V2X communication underlines the essential role of precise localization for vehicle safety, coordination, and decisionmaking in dynamic environments. Our exploration of hybrid GNSS and 5G positioning highlights how advanced localization techniques ensure the accuracy and immediacy required for V2X message exchanges in these autonomous systems. However, a reliable infrastructure is essential to achieve these high localization and real-time communication standards. Private 5G networks emerge as a necessary enabler of this framework, providing dedicated, low-latency communication that public networks can not sustain. They offer a controlled environment with customizable network resources to support V2X applications. Next Chapter 4 will explore how these private 5G networks can effectively support V2X communication, boosting spectrum allocations and configurations needed to support autonomous driving solutions, setting them as foundational to the future of connected. 4. Technology overview The preceding sections highlighted V2X communication, ITS, and precise localization as core elements of connected vehicle ecosystems. To fully realize the potential of these technologies, an infrastructure capable of supporting their demanding requirements is essential. The advent of 5G networks offers a critical foundation [100] that enhances speed, latency, and network capacity over previous generations. 5G enables real-time data exchange, low-latency communication, and massive connectivity, which are all essential for V2X applications and ITS. By facilitating high-speed and reliable connections, 5G ensures the responsiveness and precision that autonomous driving and advanced localization demand. In addition to supporting the automotive sector, 5G technology is integral to the more expansive landscape of intelligent infrastructure. This includes applications in smart cities, healthcare, and Internet of Things (IOT), which demand scalable, high-throughput communication. Thus, 5G is a foundational technology for V2X, ITS, and an array of interconnected systems, facilitating the development of nextgeneration, intelligent, and responsive environments. The 3GPP, following a consensus-based approach, is responsible for developing specifications for 3G, 4G, and 5G technologies. In early 2019, the 3GPP finalized Release 15 (Rel. 15 or R15) specifications, marking the first round of 5G standards. Rel. 15 establishes the groundwork for URLLC and principally concentrates on Enhanced Mobile Broadband (eMBB). It adds assistance for low-latency demands. The next release, Rel. 16, which served as the second phase of 5G standards, was finished in July 2020. Rel. 16 adds new capabilities such B. Mendes et al. Vehicular Communications 52 (2025) 100878 19 as non-public networks, New Radio (NR) unlicensed, NR positioning, and Integrated Access and Backhaul (IAB), in addition to improving the features already provided in the previous release [101]. Then Rel. 17, which was established in 2022, added new features like NR operation extended to 71GHz, NR over Non terrestrial Networks (NTN),IOT over NTN, UE power-saving enhancements for NR, coverage and positioning enhancements, enhanced support of non-public networks [102]. The 3GPP is now working to standardize a more recent version, Rel. 18 [103]. When this document was written, the study phase of Rel. 19 started. 5G is more adaptable than its predecessors, thanks to SoftwareDefined Networking (SDN) and Network Function Virtualization (NFV). SDN allows quicker network alterations without interfering with data transport by separating the control and data planes. NFV creates network services like firewalls and routers using software, rather than dedicated hardware. By splitting a single physical network into several virtual networks, each virtual network has dedicated resources. I.e., network slicing preserves speed and security while enabling a wide range of services. By processing data locally, edge computing lowers latency for real-time applications such as autonomous vehicles and augmented reality. As 5G technology continues to evolve with successive 3GPP releases, it also acts as an enabler for implementing 5G private networks. The deployment of these private networks greatly depends on 5G’s adaptability, which these technologies enable. 4.1. Public or private 5G networks for V2X Both public and private 5G networks offer unique advantages for the V2X applications and autonomous driving. These networks are particularly effective in addressing the specific demands of vehicular communication, such as ultra-low latency, high reliability, and seamless connectivity [104]. However, these networks differ significantly in deployment for V2X scenarios, especially considering cost and management complexity. Public 5G Networks are managed by MNO and provide broad coverage, making them an attractive option for V2X applications that require extensive reach. They offer the advantage of being maintained by operators who invest in infrastructure upgrades, ensure regulatory compliance, and deliver connectivity across large geographic areas. However, this type of network can suffer bandwidth limitations due to shared usage with other kinds of services, potentially impacting performance in high-density areas. The shared infrastructure of public networks may not always meet the latency and bandwidth requirements for V2X applications [2], especially during peak usage hours when other public users may congest the network. In contrast, Private 5G Networks (Non-Public Networks, or NPNs) are dedicated networks set up specifically for enterprises or specific applications, offering tailored control over network parameters. For V2X, private 5G networks are especially advantageous as they can guarantee dedicated bandwidth and security protocols for V2X communication [105]. This customization allows for uninterrupted service supporting autonomous driving or emergency V2V communication, without the risk of public network congestion. According to findings in various studies, private 5G networks enable operators to better control costs, performance, and coverage within specific, defined areas, thereby enhancing network efficiency for V2X applications [106, 107]. Private 5G networks provide reliability, a critical point for V2X, by ensuring that communication channels are not shared with non-critical applications. This isolation reduces the risk of packet loss or delay during high-demand periods. On the other hand, public 5G networks are generally reliable but may not meet the benchmarks required for highlevel V2X applications in all settings. Moreover, public networks face regulatory constraints and may not offer the same degree of customization and control that a private network affords. This could be a limiting factor in dynamic V2X scenarios [104]. Another point this work mentions is the need for V2X and autonomous driving for precise and reliable localization, critical for real-time maneuvering in autonomous and semi-autonomous driving scenarios. Public 5G networks provide a broad base of connectivity capable of supporting the requirements. However, they may struggle with high precision and ultra-low latency needed for real-time vehicle positioning under all conditions. Private 5G networks, by contrast, can better integrate localization, such as real-time GNSS corrections and hybrid positioning techniques. From a cost perspective, private 5G networks represent a higher initial capital expenditure (CapEx) due to the need for dedicated infrastructure and maintenance staff. However, this model can pay off in long-term operational expenditures (OpEx) savings, particularly when network slicing and virtualization techniques are employed to optimize resource allocation. These technologies are essential to private 5G networks and enable savings by facilitating efficient use of available spectrum and infrastructure. Studies have shown that, over time, private 5G networks can reduce total cost of ownership by up to 53% compared to traditional public networks, depending on the extent of virtualization and automation implemented [2,106,108]. In summary, while public 5G networks provide a costeffective solution with more coverage, private 5G networks offer a more reliable and customizable option for V2X applications where latency, dedicated bandwidth, and security are paramount. Private networks offer reliability and tailored service quality, combining lower long-term operational costs. 4.2. Private networks - A Deep discussion on V2X scenarios 5G Private networks are set to improve the digitization and realtime networking of Industry 4.0. They are critical because they enable smooth communication and data sharing across network-connected devices. This section examines the impact of 5G private networks on process automation and data insights. This requires the existence of highly reliable communications systems that can operate in an (almost) Table 9 Comparison of the Hybrid positioning techniques. Algorithm Input Data Type Simulation Type Environment Error [94] Multiple-Rate adaptive Kalman filter (MRAKF) Distance dependent model, ToA, AoA, LOS Realistic Outdoor in suburban <1 m [95] Hybrid GNSS and 5G DL-TDoA positioning LOS conditions, SNR levels DLTDoA, Simulation Outdoor in urban <5 m [96] Hybrid 5G RTT and multiconstellation GNSS solution Sky visibility, RTT, LoS con-ditions Simulation Outdoor in deep urban canyon <10 m [98] GNSS mmWave receivers +5G ToA, LOS conditions Simulation Outdoor in urban canyons or underground tunnels in vehicles <1 m [99] hybrid 5G BSs through a DTCM-KF method Sky conditions, AOD, RTT Simulation Outdoor canyons downtown in vehicles in Urban and areas <2 m B. Mendes et al. Vehicular Communications 52 (2025) 100878 20 cable-free environment since various devices must roam freely, such as automated guided vehicles in factories or autonomous driving cars on roads (which are still part of the industry 4.0 process, considering, for example, trucks transporting cargo from one factory to another). These networks differ substantially from conventional networking systems regarding service requirements [109]. Although satellite technologies can work as an extension of the main technology, they cannot act as the principal technology due to (i) the high latency of the communications link and (ii) the occurrence of nonline-of-sight for long periods. Consequently, viable alternatives are narrowed to Wi-Fi and 5G Both are competitors in what concerns technologies for vehicular communications, especially in V2V communications due to their direct and short-link nature. Still, performing well in radio handover scenarios gives a clear advantage to 5G in V2I cases. There is a need to establish 5G links between V2X cases. Still, these can be public, i.e., working in a particular spectrum whose utilization license a given operator purchased or private. The main advantage of these so-called private networks is that they can be tailor-based for specialized infrastructure and have customized settings [110]. The following advantages result from using this strategy: •Dedicated Coverage: Private 5G networks offer exclusive coverage within designated buildings or areas, ensuring uninterrupted connectivity in remote locations or those with weak public network infrastructure. •Exclusive Capacity: With a private 5G network, there is no contention for resources as experienced in public systems. The available capacity is solely allocated to the private network, eliminating congestion caused by other network users. •Intrinsic Control: Private 5G networks provide owners total control over their devices, which is impossible with public systems. Operators may improve security and data privacy by implementing their security standards, regulating user access, prioritizing traffic, and, most crucially, ensuring that sensitive data stays on their property. •Customized Service: To adapt to particular industrial applications, private 5G networks enable customization. On public networks, this level of personalization is not possible. Industrial applications may also effectively share a private 5G network, enabling optimal resource use. •Dependable Communication: Incorporating customization, intrinsic control, and support for ultrareliable low-latency communication, the devoted nature of private 5G networks enables dependable wireless communication for industrial use cases while enhancing operational dependability. By understanding the fundamentals of private 5G networks, it is now possible to analyze the architecture that underpins these systems, their designs, the offered security, and the personalized connectivity. Fig. 11. Private Mobile Networks: Deployment Options (adapted from [111]). B. Mendes et al. Vehicular Communications 52 (2025) 100878 21 4.3. Overview of 5G private network architecture When deploying a private 5G network, it is crucial to consider several variables, such as spectrum allocation, network ownership and operation, and the degree of trust between private and public network operators. There are different ways to deploy the 5G network, in [111] shows its various architectures. Four different architectures deserve to be mentioned in this work. A private 5G network in Stand-Alone (SA) deployment operates independently and establishes a personal 5G network that functions independently of an already-established public network infrastructure, Fig. 11(A). Therefore, the private network performs as a SA system, offering connectivity and services inside a particular area or company. Another type of deployment can be Shared Radio access network (RAN) with Public Network Integrated, shown in Fig. 11(B), where the private and public 5G networks share part of the RAN while the core network functions are separated. The data flow in the Private Network is restricted to that zone. Only the base stations from the private part can be configured. An additional type of deployment is a Shared RANand Control Plane with a Public Network Integrated, Fig. 11(C), where the private network shares part of the RANand the public network controls the network. Nevertheless, the private network’s whole traffic flow stays inside certain boundaries. Lastly, Fig. 11(D) shows a deployment type with Hosted by the Public Network, where the public network hosts all the networks. All the traffic flows outside of the defined premises. In Fig. 11, it is possible to analyze all the different deployments. Spectrum choice will be essential for using private networks with these architectures. Much of the spectrum has already been allocated to specific use cases or bought outright by public network operators, who then use it for their purposes. Government authorities can use the dedicated unlicensed spectrum as an alternative. Furthermore, there is a great deal of potential for the unlicensed spectrum to support the quick development of private networks. This work will mainly focus on exploring the possibilities of the unlicensed spectrum, which will be presented in the next section. 4.4. New radio unlicensed (NR-U) The 5 GHz range is just one of several frequency bands within which NR-U can work. The specifics of each band will be discussed in more detail in Section 4.4.1. It is important to note that local spectrum allocations and regional regulatory constraints may influence the specific frequency bands available by NR-Us. The NR-U mode of operation was introduced in 3GPP Release 16 [112] to enable unlicensed spectrum integration into 5G networks for cellular operations. This mode allows for such possibilities as dynamic Time-division duplexing (TDD), beamforming, and wideband carriers in unlicensed bands for uplink and downlink use. Even though it has its benefits, NR-U has some limitations compared to standard NR in the licensed spectrum. These limitations are crucial when creating applications that utilize these technologies. One of those limitations is the interference with other networks that share the same spectrum, which can be reduced if some methods are used based on the signal intensity and bandwidth. Given the many technologies with which NR-U coexists, cooperative methods are necessary. As a result, NR-U signifies a remarkable leap forward in technology by building up. NR-U builds upon the design of the physical and Media Access Control layer (MAC) layers, replacing LTE-LAA [113]. Due to the shared nature of this spectrum, it has several limitations and constraints throughout working in the range of 5.150 GHz to 6.425 GHz. Wi-Fi and other wireless technologies often use this range of bands. Some of the features of the bands in the unlicensed spectrum are shown in Fig. 12. Some studies have suggested higher frequency values [114], especially in situations where intra-network LicenseAssisted Access (LAA) Listen Before Talk (LBT) device coexistence is critical, such as in dense deployment scenarios related to 5G NR-U. The study’s conclusion emphasizes the importance of considering various class priorities and how those priorities may affect communication latency, failure rate, and channel efficiency. This understanding helps create 5G NR-U systems with more effective channel access techniques. The literature presents some studies that show the 5G NR-U as part of the future and is vital in improving several sectors. In [115] and [116], the authors show that the NR-U brings crucial characteristics for future industrial networks. There are challenges on a shared spectrum that need to be resolved using methods such as shared maximum channel occupancy time, handover skipping, self-organized networks, adaptive backoff mechanism, and multi-domain coexistence approach. This type of mechanism makes it possible to have a design using NR-U spectrum, and the considerations for coverage, interference management, and throughput help meet the industry’s specific requirements, which will be explored later in the document. The availability of this spectrum creates new opportunities for designing mechanisms and features to support bandwidth-intensive and latencysensitive applications. A great effort is needed to get the performance close to the NR [117]. However, implementing interference control algorithms and spectrum management can improve the capacity of this type of network. The main issue is that NR does not serve specific business models (which only NR-U serves), and there is also the problem of NR spectrum saturation where NR-U is of utmost importance. So, the critical (and challenging) aspect is to achieve mechanisms that bring the quality of services running on NR-U closer to the quality of services running on NR. The NR-U radio stack architecture is intended to provide seamless integration and convergence with NR services while reducing cost and complexity. The design is based on the NR radio stack with minor modifications and comprises layers such as Radio Resource Control (RRC), Service Data Adaptation Protocol (SDAP), Packet Data Convergence Protocol (PDCP), Radio Link Control (RLC), MAC, and PHY. There are challenges in implementing the NR-U, like the operation over multiple channels where NR-U employs UNII bands with primary 20 MHz channels, enabling more capacity by channel bonding, as mentioned in [118]. It allows operation on a single wideband carrier, Fig. 12. Unii bands adapted to the Europe dedicated unlicensed bands. B. Mendes et al. Vehicular Communications 52 (2025) 100878 22 which may overlap with unlicensed channels. NR-U supports multi-carrier channel access in standalone and non-standalone modes, allowing primary and secondary channel bonding, and also allows for solo operation for both master and secondary carriers in the unlicensed spectrum. The Traffic Splitter (TS) block enables NR-U and other RANs to use variable traffic splitting/convergence algorithms. Depending on the NR-U deployment situation, TS can be placed at various levels within the radio stack. GNBinitiated Control Overhead Time (COT) may be separated into DL and UL bursts in NR-U’s transmission and signal architecture. Control and data message exchange is facilitated by Physical Downlink Control Channel (PDCCH), Physical Uplink Control Channel (PUCCH), Physical Downlink Shared Channel (PDSCH), and PUSCH channels. The same OFDMmodulated waveform as NR is used in NR-U, which provides scalable numerology with varying subcarrier spacings. Control and data messages are sent in time intervals known as TTIs, and each TTI can hold up to two Transport Blocks (TBs). Because HARQ processes manage ACK feedback and retransmissions, continual TBs transmissions are possible. The numerology index defines the duration of NR-U transmissions and is divided into time slots. The CP extension is utilized for alignment and to give more time when moving between DL and UL. The RRC layer controls the CP extension, which addresses the sensing interval misalignment. NR-U differs in non-standalone mode by providing standalone operation across unlicensed bands, allowing both master and secondary cells on unlicensed carriers. Another challenge is the frequency of NR-U/ Wi-Fi coexistence, where choosing energy detection levels is a significant difficulty in NR-U design. Energy detection (ED) gives a binary indicator of channel occupancy, whereas preamble detection (PD) provides information on the device occupying the channel. Changing the detection threshold depending on device type can reduce hidden/ exposed terminals and improve the fairness of coexisting technologies. Without additional technologies, the baseline ED threshold for a 20 MHz channel is -72 dBm, which can be relaxed to - 62 dBm. It is critical to scale the ED threshold based on channel bandwidth [119]. RRC-configured UEs can change their thresholds within the constraints of regional spectrum laws, possibly allowing improved spatial frequency reuse via adaptive reinforcement learning. NR-U cells benefit from Radio Resource Management (RRM) and Radio Link Monitoring (RLM) for coordinated channel access. AP clustering and statistics monitoring can apply concepts similar to those of Wi-Fi networks, the CP-based signal detection scheme outlined in existing literature can enhance cross-technology signal detection for NR-U and Wi-Fi. Both the DL and the UL scheduling in NR-U are asynchronous, necessitating explicit notification of the time for retransmitting failed TBs or code block groups (CBGs). Three temporal delays influence DL HARQ procedures: D0, D1, and D2, whereas U0 and U1 are critical delays for UL HARQ processes.D0 and U0 indicate the initial delay before expecting an ACK/NACK following a TB/CBG transmission, including processing and feedback preparation time. D1 and U1 denote the delays between receiving a NACK and scheduling a retransmission encompassing decisionmaking and preparation intervals. D2 may involve delays related to retransmissions or receiver processing time postretransmission. Properly configuring these delays facilitates retransmissions, maximizes spectrum utilization efficiency, reduces interference, and boosts throughput for both NR U and Wi-Fi systems. Effectively managing these parameters is vital for maintaining the coexistence of communication standards, ensuring that NR-U system can efficiently handle its transmissions and retransmissions in a shared environment, keeping these time delays inside the gNBinitiated/UEinitiated COT border. The TBs can be broken into CBGs, allowing the PHY layer to handle TBs and CBG-based HARQ designs. More research is needed to determine the influence of these designs on NR-U/Wi-Fi coexistence. HARQ additions include dynamic codebook use, one-time feedback requests, and the option to specify the timing of ACK feedback as ’later.’ Identify events spanning many COTs, and PDSCH events are indexed using the DL assignment index (DLI) and PDSCH group bits. Because NR-U’s UL scheduling considers unstable unlicensed channels, a single UL grant can schedule numerous UL TBs for the same UE. Configured UL grants allow UEs to utilize UL resources without a grant, decreasing signaling overhead and increasing channel access options. NR-U also allows transmitting sounding reference signals on every suitable UL OFDM symbol, facilitating wideband channel estimation. The literature contains detailed information on DL and UL resource mapping and allocation in NR-U. In the RRC layer, the UE connects to the gNB with the strongest signal, where synchronization is maintained during initial access. Every 20 milliseconds, the discovery frame is broadcast periodically and contains SSB bursts that carry crucial data for UEs to attach to unlicensed cells. The discovery frame ensures speedy initial access. To find a cell, the UE watches for SSB instances. Depending on SCS, the discovery frame may occur in 10 or 20 possible locations during a 5-millisecond burst window. After receiving the discovery frame, the UE starts a Random Access Channel (RACH) procedure with the best gNB using a 2message or 4-message handshake procedure, depending on connectivity status. There could be more than one COT in the RACH process. The periodicity of the RACH procedure and discovery frame is optimized to support correct initial access without interfering with IEEE 802.11 systems [120]. NR-U incorporates additional paging occasions to address channel unavailability. There are different ways to deploy the NR-U. In [112], three distinct ways are shown. There is Carrier Aggregation, Dual Connectivity, and the Standalone mode. [120] and [121] thoroughly describe these different methods. The Carrier Aggregation method is considered where the unlicensed spectrum is used only to augment downstream user plane capacity. The Control Plane (CP) data is transported over NR licensed spectrum only. Then, the Dual Connectivity method supports upstream and downstream user plane traffic over the unlicensed spectrum. This is designed for traffic offload, not coverage, so control plane traffic is transported only over the licensed spectrum. The Carrier Aggregation mode improves throughput, while the Dual Connectivity mode enhances both throughput and reliability. However, the DC (Dual Connectivity) mode introduces the complexity of associating a UE with multiple cells. In the DC mode, if the master link fails, the secondary links remain unaffected. Depending on the utilization of DC and CA for connecting with UEs over unlicensed carriers, 3GPP provides flexible deployment options for NR-U. Finally, the last method is the Standalone, a mode of operation that relies solely on the unlicensed spectrum for control and user plane traffic. Apart from the deployments mentioned, other deployments are shown in Fig. 13. Scenario A) shows that the CA mode comprises a licensed carrier provided by a 5G NR cell and an unlicensed carrier served by a 5G NR-U cell. Scenario B) demonstrates that the DC mode consists of a licensed carrier served by an LTE cell and an unlicensed carrier served by a 5G NR-U cell. The Standalone mode, essential for operating private networks, involves an unlicensed carrier(s) served by a 5G NR-U cell, represented in scenario C). Scenario D) shows the combination of a licensed carrier served by a 5G NR cell for uplink communication and an unlicensed carrier served by a 5G NR-U cell for downlink communication. Finally, in Scenario E), in DC mode, a licensed carrier is served by a 5G NR cell, while an unlicensed carrier is served by a 5G NR-U cell [122]. Table 10 shows a summary of the pros and cons of each of these deployments. Literature such as [123] and [124] study the potential of spectrum usage regulation taxonomies for ITS in vehicular communications. It shows possible solutions for standards like DSRC, C-V2X, Wi-Fi standards, and the technical challenges and testbeds associated with each option. The NR-U systems must comply with spectrum regulations to ensure harmonious coexistence with other incumbents, such as Wi-Fi. The article presents three possible deployments the DSRC, C-V2X, and Wi-Fi with interference mitigation, or DSRC, and C-V2X with B. Mendes et al. Vehicular Communications 52 (2025) 100878 23 interoperation or C-V2X only. The interoperability between DSRC and C-V2X is identified as the most complex technical challenge. The choice of spectrum usage regulation taxonomies will depend on each country’s specific plans and priorities involved in standardization. Sidelink Communication allows for direct communication between V2V and V2I; this technology can make maximum use of the NR-U. This facilitates sharing realtime vital messages such as road conditions, dangers, and potential accidents, thereby enhancing road safety and efficiency. Sidelink follows specific protocols that ensure efficient and reliable sidelink conversations while considering factors such as latency (speed of delivery), information security, and efficient use of the radio spectrum. The technology allows the UE to be used as a gNB for other UE, causing their communication. For this to work, several channel access mechanisms must be implemented so that the radio spectrum is shared among vehicles in an orderly manner without congestion involving all road users. NR-U is being researched into [125,126], where these bands have several benefits for this communication. Shorter-range communications at higher frequencies cause signals to attenuate more rapidly, making them ideal for local communication between nearby vehicles and infrastructure, reducing interference with longhaul cellular traffic. Above all else, they are critical because these bands are now available worldwide, making them a viable option for standardizing V2X deployments. As seen so far, NR-U brings a severe problem with interference between signals, and for that, different methods can be applied. LBT can significantly affect channel efficiency and latency, especially when timely message transmission is critical. LBT is increasingly important in applications like IOT networks, because of its more straightforward implementation and less resource usage. On the other hand, other algorithms, such as Cooperative Multipoint (CoMP), are required in vehicular networks where latency minimization is crucial. Despite being more intricate and requiring more resources, these algorithms guarantee optimal performance and minimal delay. The Section 4.4.2 will explain these interference reduction algorithms more thoroughly. 4.4.1. Regulation bodies Different countries and regions designate specific frequency channels or sub-bands for NR-U operation within the spectrum. Regulatory bodies choose the frequencies and standards for NR-U operation based on spectrum availability, interference control, and compatibility with other wireless technologies. Any device or operator can use this frequency band if it complies with the regulations and usage rules set by the regulatory bodies. By using unlicensed spectrum, operators can use NR to provide 5G connectivity in areas where licensed spectrum is saturated or unavailable or to give connection to small businesses like factories, universities (campus), IoT Agriculture systems, and more. Countries and regions may have different frequency channels or sub-bands designated for NRU operation in the spectrum; for example, there are different legislations for each world area, including the USA, EU, China, and Russia. In the EU, legislation related to the automotive spectrum and applications follows specific guidelines. In the context of connected vehicles and ITS, there are two distinct groups: critical and non-critical systems. The EU has defined specific spectrum bands for critical ITS systems requiring high reliability and safety. In the public spectrum band, the 2.6 GHz range is reserved for critical ITS communications. This frequency band enables the implementation of C-V2X communication services for applications requiring fast response times and reliable communications in safety-critical situations. In addition, the EU has also designated the 5.8 GHz spectrum band for critical ITS communications on a confidential basis [8]. Different ways to set the radio bands on the unlicensed spectrum, such as Unlicensed National Information Infrastructure (U-NII), can give us that information. These bands are intended to provide additional spectrum for unlicensed use, which helps promote innovation without the need for individual licenses. The U-NII bands are divided into subbands or channels, each with specific frequency ranges typically operating in the 2.4 GHz and 5 GHz [127]. The detailed U-NII bands information for its use and the comparison with the EU legislation can be analyzed in Table 11, where it is shown different bands, services location, and their restrictions. Fig. 13. NR-U deployment scenarios (adapted from [120] [122],). Table 10 Pros and Cons for different NR-U deployment scenarios. Implementation Scenario Pros Cons Carrier Aggregation Increased data rates Complexity and cost Enhanced user experience Interference concerns LTE Dual Connectivity Efficient spectrum utilization Compatibility limitations Seamless transition Complexity and cost Improved coverage Interference and compatibility challenges Standalone Mode Enhanced capacity Limited benefits for LTE-only devices Native NR-U support Infrastructure requirements Lower latency Interoperability challenges DL-UL Decoupling Flexibility and scalability Limited coverage Improved capacity management Increased complexity Enhanced user experience Interference and synchronization challenges NR-NR Dual Connectivity Quality of Service differentiation Device support Enhanced capacity and performance Deployment complexity Seamless handover and mobility Interference and compatibility issues Flexibility and efficiency Limited device support B. Mendes et al. Vehicular Communications 52 (2025) 100878 24 This Table 11 shows that the only band compliant with certain vehicular use cases is U-NII-4. This work will focus on using NR-U in vehicular communication, but there is a more dedicated spectrum for other areas that can be seen on [128]. For critical vehicular communications, the EU legislation limits the maximum channel bandwidth to 10 MHz in this band and the maximum total transmit power to 33 dBm with a transmit power control (TPC) range of at least 30 dB More dedicated bandwidth exists for non-critical vehicular communications, but the signal characteristics remain the same. The distribution of the 5 GHz unlicensed spectrum varies among nations, each with unique regulations and usage restrictions. There is notable availability in the USA, China, South Korea, Europe, Japan, and India for the 5.15–5.35 GHz (UNII–1 and U–NII–2A) band. It is possible to access the 5.47–5.725 GHz (U-NII-2C) spectrum in the USA, South Korea, Europe, and Japan. The USA, China, South Korea, and India also have unrestricted access to the 5.725–5.85 GHz (U–NII–3) band. The unlicensed 5.15–5.35 GHz and 5.47–5.85 GHz bands in the United States and Canada support wireless access. On the other hand, these bands are reserved for the Wireless Access System (WAS) and Radio Local Area Networks (RLANs) in Europe and Japan. China uses the 5.15–5.35 GHz band for interior applications and the 5.725–5.85 GHz band for outdoor ones. The 5.35–5.47 GHz (U-NII-2B) and 5.85–5.925 GHz (U-NII-4) bands in the USA and Canada. For example, the FCC proposed in its Notice of Proposed Rulemaking (NPRM) 13–22 to open up 195 MHz in the 5.35–5.47 GHz band for radar system mergers and in the 5.85–5.925 GHz band for ITS mergers. The European Commission has also proposed using WAS/ RLANs on the 5.725–5.85 GHz spectrum for Fixed Wireless Access (FWA). 4.4.2. Mechanisms of interference reduction As can be inferred from what has been described, one serious problem in using NR-U is the interference between different technologies, such as WIFI, that fall in the same network spectrum. So, developing contention mechanisms is needed to diminish the interference and packet loss that can become a severe problem in several use cases. There are several interference types, like Co-Channel Interference and Adjacent Channel Interference [124]. When NR-U emissions from one channel cross into nearby channels, adjacent channel interference occurs. This interference may be caused by poor filtering or insufficient guard bands between neighbouring channels, which will cause signals to overlap and degrade the quality of the adjoining channel. It occurs when many NR-U cells or nodes using the same channel interfere with one another. Reduced data and higher error rates might result from this interference, affecting signal quality and overall system performance. A channel access method is considered LBT in use by NR-U to operate over unlicensed UNII bands below 7 GHz. These open and shared frequency bands mean coexistence with technologies like LTE-LAA or IEEE 802.11 is possible. Devices determine when to transmit by listening for several idle slots proportional to a contention window (CW). This approach takes after the CSMA/CA protocol, which employs an exponential backoff mechanism, to adaptively set the size of CW depending on the network status. Collusion rates and channel access latency are two essential network performance parameters directly affecting the contention windows. Two parameters define CW: CWmin and CWmax, which are the lower minimum and upper maximum limits for the size of the Contention Window, respectively. At first, the window size doubles from CWmin after every failed attempt until it reaches CWmax, significantly reducing repeated collisions in congested areas only. One of the key technologies used by IEEE 802.11 (Wi-Fi) systems is Enhanced Distributed Channel Access (EDCA). This technique prioritizes traffic into four categories, characterized by different CWmin, CWmax, and Arbitration Interframe Space (AIFS) values. This setting accommodates various application needs, such as voice calls with minimal delays and bulk data transfer. Other features like Transmission Opportunity (TXOP) periods and block acknowledgments enhance throughput and efficiency. On top of that, enhancements brought about in IEEE 802.11ax include Uplink/ Downlink (UL/DL) split with Orthogonal Frequency-Division Multiple Access (OFDMA). These improvements further enhance network efficiency while allow-ing more devices to be supported. LTE-LAA and NR-U have established LBT Categories (CATs), which are used to differentiate between different channel access schemes. LTELAA and NR-U mainly adopt CAT4-LBT, which has similarities with CSMA/CA mechanisms. In this case, contention window size is adjusted dynamically based on network feedback to achieve better coexistence with other systems in the unlicensed spectrum. NR-U also has multiple priority classes similar to the EDCA mechanism in Wi-Fi, enabling differentiated traffic handling according to its priority levels. To ensure a more deterministic or critical communication, CAT2LBT can provide semi-static channel access, which is crucial for transmitting important and timesensitive frames. The operational dynamics like timing, UL/DL configurations, and Hybrid Automatic Repeat Request (HARQ) processes in LTE-LAA and NR-U have differences that are related to their adaptation to unlicensed spectrum requirements. NRU is uniquely designed for gNB-initiated COT for dynamic UL/DL switching to enhance flexibility and resource use efficiency. However, the system currently supports only UL-to-DL switching from the user equipment side. In these systems, control channels and data channels (PDCCHs and PDSCHs for downlink control and data, PUCCH and PUSCH for uplink control and data transmissions, respectively) are vital elements that enable effective communication between network part radios with UE at boundaries of licensed radio bands as well as other possibilities of such unlicensed spectrum-based services. The type of synchronism or asynchronism is essential, depending on the chosen use case. Choosing a type of asynchrony is of the utmost importance, such as the reliability of the service is terrible, as it is always waiting and only then occupies the channel and sends. In some cases, it Table 11 NR-U bands with the dedicated services and legislation associated. Freq. Range [MHz] Band Service char. Restrictions EU legislation 5 150 to 5 250 UNII-1 Indoor and outdoor Outdoor installation cannot be fixed 2022/179/ EU 5 250 to 5 350 UNII2a Indoor use only Installations in road vehicles not per-mitted 5 470 to 5 725 UNII2c Indoor and outdoor Installations in road vehicles not per-mitted 5 725 to 5 875 UNII2c Non-specific short-range equipment 2019/ 1345/EU 5 795 to 5 815 UNII-3 Road toll applications and intelligent tachograph applications, weights and dimensions 2019/ 1345/EU 5 855 to 5 875 UNII-4 ITS non-safety applications ECC/REC/ (08)01 2019/ 1345/EU 5 865 to 5 875 UNII-4 ITS non-safety applications V2V, V2I, and infrastructure-to-vehicle systems 2019/ 1345/EU 5 875 to 5 915 UNII-4 ITS road safetyrelated applications 2020/ 1426/EU 5 915 to 5 925 UNII-4 ITS road safetyrelated applications Limited to applications involving I2V connectivity only  5 945 to 6 425 UNII-5 Indoor and outdoor Outdoor use must be very low power to cover shortrange applications for small area direct communications. 2021/ 1067/EU B. Mendes et al. Vehicular Communications 52 (2025) 100878 25 may not send anything during some time. With a synchronous system, it is possible to maintain order between the waiting and sending times, thus resulting in greater efficiency for its use. In Fig. 14, it is possible to verify the behaviour of both types of spectrum sharing. Some types of methods were created to diminish the interference, such as the asynchronous spectrum sharing type (e.g., LBT and some divergent from it like omniLBT, dirLBT, pairLBT, and LBTswitch [130]), as well as of the synchronous spectrum sharing type, such as CoMP. The LBT mechanism is employed by unlicensed devices, such as WiFi and NR-U, to ensure fair spectrum sharing and minimize interference with other devices [124]. It requires a device to check for active broadcasts on the channel before starting its transmission. Devices can prevent interfering with other technologies using the same unlicensed frequency bands by using LBT. [124] presents a vast literature with several vital points explaining different methods of interference management, like the LBT used in previous 3GPP-defined unlicensed technologies, and it will always depend on the regulation of the implemented zone. Research challenges related to coexistence, interference, and harmonious operation are identified. This study makes a significant point for applying NR-U in the vehicular world. There is a continuing study in the literature on neighbouring channel interference and how it may affect applications made possible by V2X communications. However, the importance of this interference is still up for debate. Therefore, it is crucial to undertake research that shows whether unlicensed devices can function in the U-NII band’s lower channels without impairing the functionality of V2X Radio Access Technologies (RATs). There is a need for testbeds where it is possible to simulate environments with factors such as device density and locations for both V2X and unlicensed devices, typical transmit powers of V2X, and unlicensed transmitters. These realistic propagation models reflect real-world scenarios. This will be the most important to prove the reliability of this service with this dedicated spectrum. [122] proposes a simulation to evaluate the different LBT-based channel access procedures for NR-U/Wi-Fi indoor mmWave coexistence scenarios is also provided. It presents different requirements for the harmonization of 5 GHz usage. This network needs to follow the LBT, the Maximum Channel Occupancy Time (MCOT) where the system can not make continuous transmissions, and the existence of limits that a device can use. For others, such as Dynamic Frequency Selection (DFS) functionality that avoids interfering between 5 GHz and 60 GHz radar systems, the objective is to spread the traffic load across the different channels uniformly. This work simulates different scenarios to test different LBT specifications. This shows that the traditional methods are insufficient for this kind of solution. Hence, the LBT for the NR-U had to be improved using other methods like the LBT for Beam-Based Transmissions, Receiver-Assisted LBT for Beam-Based Transmissions, Intra-RAT Tight Frequency Reuse and Congestion Window Size (CWS) Adjustment for Beam-Based Transmissions. In summary, the research demonstrates that incorporating receiver feedback enhances coexistence performance in 5G NR-U, improving QoS and fairness. Regarding the COT structures, it is found that slots with multiple DL/UL switching points within the COT are better suited for NR-U. For the initial access in NR-U, careful design considerations are discussed for SS block design, RACH procedure, and paging procedure to address the requirements of LBT effectively) and Out-of-Channel Blocking (OCB). These considerations aim to optimize the performance and efficiency of 5G NR-U regarding coexistence and initial access. Other studies, such as [131], show different approaches to the interference fairness between Wi-Fi and NR-U where they propose a ML model to channel allocation. It was used as a testbed created and evaluated where legacy methods were tested to compare with their approach. The result shows that the approach is beneficial for long synchronization periods since the behaviour between Wi-Fi and NR-U converges for short synchronization periods. This work was not conclusive, but it leaves a loophole that can be studied. However, these algorithms are insufficient to cover cases such as networks with autonomous vehicles, so other methods are needed. Such methods of Synchronous spectrum sharing like the technology of CoMP are applied in 5G networks to improve coverage, capacity, and performance. Multiple base stations or cells coordinate their transmission and reception in this situation. It can increase system efficiency, reduce interference, and improve signal quality. Even while CoMP does not address technology interference specifically, its advantages in terms of interference reduction can indirectly aid in more harmonious coexistence with other technologies in the unlicensed spectrum. Releases 16 of 3GPP [112] introduced the possibility of transmission and reception at multiple transmission and reception points on the network side, a mechanism called CoMP [132], where a device is transmitted via a variety of transmission points, which may switch between one another dynamically or be used collectively from several sites. Thus, providing URLLC services in NR-U with synchronized spectrum sharing and CoMP is viable. 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