Performance analysis of iteratively decoded convergent source mapping with sphere packing-assisted differential space-time spreading technique for efficient video transmission

Research Article Performance Analysis of Iteratively Decoded Convergent Source Mapping with Sphere Packing-Assisted Differential Space-Time Spreading Technique for Efficient Video Transmission Ishtiaque Ahmed , 1 Nasru Minallah , 2 Jaroslav Frnda , 3 and Jan Nedoma 4 1 National Centre in Big Data and Cloud Computing, University of Engineering and Technology Peshawar (NCBC-UETP), Peshawar 25000, Pakistan 2 Department of Computer Systems Engineering, University of Engineering and Technology Peshawar, Peshawar 25000, Pakistan 3 Department of Quantitative Methods and Economic Informatics, Faculty of Operation and Economics of Transport and Communications, University of Zilina, Zilina 010 26, Slovakia 4 Department of Telecommunications, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, Ostrava-Poruba, Czech Republic Correspondence should be addressed to Ishtiaque Ahmed; ishtiaqueahmed20[email protected] Received 10 August 2021; Revised 2 December 2021; Accepted 11 December 2021; Published 29 December 2021 Academic Editor: Lucia Valentina Gambuzza Copyright ©2021 Ishtiaque Ahmed et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. With the substantial growth in number of wireless devices, future communication demands overarching research to design highthroughput and efficient systems. We propose an intelligent Convergent Source Mapping (CSM) approach incorporating Differential Space-Time Spreading (DSTS) technique with Sphere Packing (SP) modulation. The crux of CSM process is assured convergence by attaining an infinitesimal Bit-Error Rate (BER). Data Partitioning (DP) H.264 video codec is deployed to gauge the performance of our intelligent and efficient system. For the purpose of efficient and higher data rates, we have incorporated compression efficient source encoding along with error resiliency and transmission robustness features. The proposed system follows the concept of iterations between the Soft-Bit Source-Decoder (SBSD) and Recursive Systematic Convolutional (RSC) decoder. Simulations of the DSTS-SP-assisted CSM system are presented for the correlated narrowband Rayleigh channel, using different CSM rates but constant overall bit-rate budget. The SP-assisted DSTS systems are mainly useful in decoding algorithms that operate without requiring Channel State Information (CSI). The effects of incorporating redundancy via different CSM schemes on the attainable performance and convergence of the proposed system are investigated using EXtrinsic Information Transfer (EXIT) charts. The effectiveness of the proposed system is demonstrated through IT++ based proof-of-concept simulations. The Peak Signal-to-Noise Ratio (PSNR) analysis shows that using Rate-2/6 CSM with minimum Hamming distance (dH, min) of 4 offers about 5 dB gain, compared to an identical overall system code rate but with Rate-2/3 CSM and dH, min of 2. Furthermore, for a consistent value of dH, min and overall rate, the Rate-2/3 CSM scheme beats the Rate-5/6 CSM by about 2 dB at the PSNR degradation point of 2 dB. Moreover, the proposed system with Rate-2/3 CSM scheme furnishes an Eb/N0gain of 20 dB when compared with the uniform-rate benchmarker. Clearly, we can say that higher dH, min and lower CSM values are favourable for our proposed setup. 1. Introduction Evolution in Internet of Things (IoT) and recent developments in ultrafast cellular technologies resulted in a constriction of available bandwidth [1, 2]. It is estimated that escalation in the demand of higher data rates and bandwidth efficient systems will continue to grow with the advent of Fifth-Generation (5G) wireless technology. 5G is expected to revolutionize the outlook of mankind in medical [3] and intelligent communication technologies [4]. Thus, further researches need to be carried out to better cope with the future demands of wireless and multimedia services. Hindawi Complexity Volume 2021, Article ID 5776480, 16 pages https://doi.org/10.1155/2021/5776480 Traditionally, source coding standards and channel coding schemes have been incorporated in multimedia systems for the transmission of voice and video streams. Source coding is an important approach to compress the original stream of data, focusing on the removal of redundancy from the stream for transmission over bandlimited channels. The unwanted concomitant is that removal of redundancy results in the hostile happening of unreliability in the transmitted data. The main reason for such unreliability is that transmission over a nonideal (practical) channel triggers distortions in the transmitted signal. Very few or no-redundant bits schemes neither detect nor correct any potential error arising from unreliable transmissions. Therefore, multimedia streams present an exacting research topic for wireless channels [5]. To address the issue of unreliability, channel coding is utilized. Channel coding focuses on the addition of redundant bit(s) to make the transmission scheme reliable. Consequently, the concept of Joint Source and Channel Decoding (JSCD), making the most of residual redundancy phenomenon, has remarkably attracted wide attention [6]. The approach of Soft-Bit Source-Decoder (SBSD) was proposed [7] and effectively deployed in Iterative Source and Channel Decoding (ISCD) [8] for better convergence. More specifically, SBSD operates by gleaning the residual redundancy in the source coded stream for yielding extrinsic information, thus transforming the stream into short frames. The authors in [9] proposed two techniques, namely, I-frame and P-frame error concealment methods, for robust video transmission over noisy channels. Several other methods are presented in [5] to overcome the exigent tasks of susceptible transmission and channel errors due to the limited residual redundancy and predictive coding. Advanced Video Coding (AVC) or H.264 video codec is currently the most popular compression standard among the researchers for mobile video systems. The strategy of Data Partitioning (DP) [10] is advantageously subsumed in the H.264 standard, reducing the unfriendly channel effects corrupting the data stream. DP splits the stream in line with the level of significance of data into three substreams, each having specific level of importance. Another approach known as Irregular Variable Length Coding (IrVLC) was efficiently utilized in schemes with joint source and channel coding [11]. In short, researchers continuously strive and have put persistent efforts to hone the overall wireless communication infrastructure by making it incrementally closer to Shannon’s capacity [12] pinnacle. Multiple-Input Multiple-Output (MIMO) system technologies are growingly becoming popular in bandwidth efficient systems supporting ultrafast rates. Such technologies will be of great significance in the future wireless systems such as 5G and 6G [13]. One of the prestigious classes of MIMO systems is Space-Time Block Codes (STBCs), offering fairly acceptable performance with a much easier approach of encoding and spreading [14, 15]. Space-Time Spreading (STS) technique was presented by Hochwald et al. [16] attaining the best possible transmit diversity gain. The authors in [17] developed an orthogonal transmit diversity technique using Sphere Packing (SP) modulation and demonstrated that the SP-assisted STBC exceeds the conventional STBCs in performance. The STBC and STS approaches rely mainly on the technique of coherent detection, which in turn requires Channel State Information (CSI) at the receiver [18]. Besides this, CSI burdens the overall system which dissipates power due to the enormous transmission of training symbols. An alternate to CSI based systems, known as Differential Space-Time Block Coding (DSTBC), was proposed by Tarokh et al. for the cases of two and more transmit antennas [19, 20]. DSTBC greatly reduced the complexity but the price paid was a slight loss in performance. SP-assisted DSTS approach was deployed to enhance the performance of adaptive multirate wideband speech coding [21], Irregular Variable Length Coding with iterative detection [22, 23], cooperative communication [24], self-concatenated coding [25], and turbo detection [18] systems. The performance analysis of convergent and nonconvergent two-stage DSTS-SP schemes based on video resolution and motion contents was done in [26]. The three-stage performance investigation of compressed video using SP modulation was done for the combinational gain technique of layered steered space-time coding in [27], whereas that for the noncoherent DSTS scheme was presented in [28]. In this letter, we apply the technique of DSTS-SP to CSM and investigate its effects on the Bit-Error Rate (BER) performance and EXtrinsic Information Transfer (EXIT) curves. The main contributions of our research work are as follows: (i) An instructive preamble to the concept of CSM and its linkage with RSC, devised for attaining assured convergence. (ii) Incorporating the technique of DSTS-SP in CSM scheme and investigating its effects using two Transmitters (Txs) and a simple Receiver (Rx) with no CSI requirements (iii) Analysing the effects of alterations in CSM rates and Hamming distance (dH, min) on the EXIT convergence and, hence, attainable performance of the proposed system (iv) Quantifying the video performance of the presented system with the H.264 standard The remainder of this article is organized as follows. Section 2 gives details about the parametric parlance used in this discourse. An overview of the DSTS-SP based CSM approach is provided in Section 3. The proposed system with the parameter settings is further described and made plain in Section 4. Simulated results are discussed in Section 5. We succinctly conclude the article in Section 6. 2. Parametric Terminologies Logarithmic-Likelihood Ratio (LLR) or L-value is an important parameter based on the Soft Decision (SD) output and is mainly used to estimate the reliability of received data. In an SD process, the messages are represented in conditional probabilities of their occurrence, such that the 2Complexity received bit is marked either 1 or 0. The LLR is defined as in the following equation [29]: LX(u) � ln PX(u�0) PX(u�1) 􏼠 􏼡.(1) Here, LX(u)is the LLR or soft value of a random variable Xand PX(u)represents the probability of Xfor its two legitimate values. One of the key solutions for future ultrafast wireless communication is the MIMO technology. MIMO concept mainly asks for utilizing multiple antennas at base stations to serve a vast range of users connected via single receiving antenna or multiple receiving antennas installed in their devices. There arise some issues like hardware complexity, channel estimation, and correlation of the multiple antennas [30], when considering MIMO schemes. DSTS technique offers a good solution to the issues in MIMO systems and advocates the employment of MIMO schemes for attaining rich diversity gains. Spreading codes are utilized in the systems having MIMO. An STS technique with two transmitters is considered in the proposed system of this treatise. STS algorithm basically splits the data stream into multiple substreams (equal to the number of transmitters) and each substream is transmitted by an assigned antenna. Walsh codes are widely exploited as spreading codes in wireless MIMO schemes, specifically in synchronized multiuser systems. Walsh codes are based on the mutually orthogonal Hadamard codes, providing frequency diversity and very good BER performance. Hadamard matrix is necessarily a square matrix generating Walsh code by the continuous conversion between ON and OFF (1 and −1) states at a specified regular transition interval [31]. Details about the operation and generation of different length Walsh codes using Hadamard matrix are discussed in [32]. SP modulation is scaling up in the construction of errorcorrection codes. Su et al. introduced the merger of transmit diversity techniques with SP modulation, evincing that the SP aided STBC surpassed the conventional STBCs in performance [18]. The viable profit of orthogonal transmit diversity is mostly evaluated using the minimum Euclidean distance [18]. SP modulation results in the best possible minimum Euclidean distance for the symbols, boosting the system’s error resilience property. Regarding Euclidean distance, it is simply the length of straight-line segment connecting two coordinates. Hamming distance C A B Source Coding A B C Partitions H.264 Encoder Concatenate A CB ACB DeMUX Rx1 H.264 Decoder Tx1 Tx2 DSTS Decoder DSTS Encoder Deconcatenate Information Bits MUX Decoded Bits Π−1 Rayleigh Channnel Iterative Decoding Π SBSD RSC Encoder yi SP Mapper si RSC Decoder SP Demapper Π xixi x′ i xk LSBSD (xm) extr LSBSD (xm) apr LRSC (xm) apr LRSC (xm) extr L (yi) CSM Encoder CSM Decoder L (⌃x′ m) ⌃xi ⌃xk⌃si Figure 1: Block diagram of the proposed DSTS-SP aided iterative JSCD system. Table 1: Proposed DSTS-SP-assisted CSM system parameters. Parameters Value Parameters Value Source code H.264/AVC Channel code CSM Source bit rate 64 kbps Tx antennas 2 Video sequence QCIF Akiyo Rx antennas 1 Frame rate 15 fps RSC generator (G0, G1, G2, G3�13,15,15,17)8 Slices per frame 9 Interleaving bits 10000 Number of MBs per slice 11 Normalised Doppler frequency 0.01 Intraframe MB update 3 Modulation scheme SP MIMO scheme DSTS Spreading code Walsh code STS Encoder DSTS Encoder Delay Differential Encoder q si Figure 2: Schematic diagram of the DSTS encoder. Complexity 3 Table 3: CSM-coded symbols with dH, min and corresponding code rates of constituent encoders. Error protection scheme Symbols in decimal dH, min Code rate CSM RSC Overall Rate-1 (benchmarker) 0, 1 1 1 1/4 1/4 CSM3 2, CSM4 3, CSM5 4, CSM6 5 0, 3, 5, 6, 0, 3, 5, 6, 9, 10, 12, 15, 0, 3, 5, 6, 9, 10, 12, 15, 17, 18, 20, 23, 24, 27, 29, 30, 0, 3, 5, 6, 9, 10, 12, 15, 17, 18, 20, 23, 24, 27, 29, 30, 33, 34, 36, 39, 40, 43, 45, 46, 48, 51, 53, 54, 57, 58, 60, 63 2, 2, 2, 2 2/3, 3/4, 4/ 5, 5/6 3/8, 1/3, 5/ 16, 3/10 1/4, 1/4, 1/ 4, 1/4 CSM6 2, CSM8 3, CSM10 4, CSM12 5 0, 30, 45, 51, 0, 60, 90, 102, 153, 165, 195, 255, 0, 120, 180, 204, 306, 330, 390, 510, 561, 585, 645, 765, 771, 891, 951, 975, 0, 240, 360, 408, 612, 660, 780, 1020, 1122, 1170, 1290, 1530, 1542, 1782, 1902, 1950, 2145, 2193, 2313, 2553, 2565, 2805, 2925, 2973, 3075, 3315, 3435, 3483, 3687, 3735, 3855, 4095 4, 4, 4, 4 2/6, 3/8, 4/ 10, 5/12 3/4, 2/3, 1/ 4, 3/5 1/4, 1/4, 1/ 4, 1/4 IE (inner)/IA (outer) OUTER EXIT CURVES Rate-1 Mapping Rate-2/3 CSM Rate-3/4 CSM Rate-4/5 CSM Rate-5/6 CSM Rate-2/6 CSM Rate-3/8 CSM Rate-4/10 CSM Rate-5/12 CSM 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.00.0 IA (inner)/IE (outer) 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 Figure 3: EXIT curves for error protection schemes employing different outer (CSM) and inner (RSC) rates. Table 2: Different combinations of Mapping-I and Mapping-II schemes. Input symbols Mapping-I scheme Mapping-II scheme S (1) ,S (2) .S(k 2)r1b1b2...bk,r2b1b2. . . bk,r2Kb1b2...bkr1b1b2...bkbk. . . b2b1r1,r2b1b2. ..bkbk. . . b2b1r2,r2Kb1b2. . . bkbk. . . b2b1r2K 4Complexity terminology, proposed by Hamming [33], is frequently used in the realm of error-correcting codes. Hamming distance quantifies and corrects the emerging error due to noisy channel when data bits are transmitted to the destination. More specifically, Hamming distance shows the difference in two strings with same dimension, that is, the number of varying places in two strings (bitwise exclusive OR, commonly known as XOR) [34]. For example, 10110100 and 10000111 differ in 4 bit places and hence a Hamming distance of 4 exists between them. Similarly, “ROSE” and “NOSE” are single character apart, so a Hamming distance of 1 applies here. EXIT chart analysis, proposed by Stephen ten Brink [35], promptly predicts the convergence behaviour of iteratively decoded systems. EXIT curves are based on mutual information exchange between the constituent decoders of iterative systems [36]. EXIT charts serve as a handy replacement to the cumbersome Monte Carlo based simulation, offering accurate results for attaining infinitesimal BER [37]. Some common terminologies associated with an EXIT analysis are a priori Information (IA), a posteriori LLR, and Extrinsic Information (IE). As given in [37], IAis the already known intrinsic knowledge of any bit and its value is independent of the iterative decoding operation. When any decoder accepts input in the form of LLRs from channel and IA, the decoder’s output is termed as a posteriori LLR. Performing a mathematical operation of subtracting IAfrom the first output of concerned decoder yields IE. It is worth mentioning that IEcan be manipulated with the aid of interleavers and deinterleavers, converting it to IAfor other input instances to decoder. EXIT chart analysis asks for two factors in order to generate accurate results [37, 38]. Firstly, the a priori LLRs should be greatly uncorrelated by utilizing higher interleaving bits and, secondly, the probability density function (PDF) of such LLRs must be Gaussian distribution. The following relations as given in [37] present a clear view of the concepts and terminologies linked with an EXIT analysis. 0≤IA≤1,(2) 0≤IE≤1,(3) IE�T IA,Eb N0 􏼠 􏼡,(4) T(0)≤IE≤T(1).(5) According to equations (2) and (3), the values of IAand IErange from 0 to 1. IAto IEconversion requires a transfer function Toperating at specific Eb/N0, as given in equation (4). The inverse of Texists on the range specified in equation (5). Rate-2/3 Outer CSM Rate-3/8 Inner RSC Eb/N0 = -3 & -4 dB IE (inner)/IA (outer) 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.00.0 IA (inner)/IE (outer) Figure 4: EXIT chart curves and decoding trajectories for Rate-2/3 CSM (Mapping-I) scheme. Complexity 5 Rate-1/3 Outer CSM Rate-3/4 Inner RSC Eb/N0 = -6 & -6.5 dB IE (inner)/IA (outer) 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.00.0 IA (inner)/IE (outer) Figure 5: EXIT chart curves and decoding trajectories for Rate-2/6 CSM (Mapping-II) scheme. Rate-3/4 Outer CSM Rate-1/3 Inner RSC Eb/N0 = -3 & -4 dB IE (inner)/IA (outer) 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.00.0 IA (inner)/IE (outer) 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 Figure 6: EXIT chart curves and decoding trajectories for Rate-3/4 CSM (Mapping-I) scheme. 6Complexity Rate-3/8 Outer CSM Rate-2/3 Inner RSC Eb/N0 = -6 & -6.5 dB IE (inner)/IA (outer) 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.00.0 IA (inner)/IE (outer) 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 Figure 7: EXIT chart curves and decoding trajectories for Rate-3/8 CSM (Mapping-II) scheme. Rate-4/5 Outer CSM Rate-5/16 Inner RSC Eb/N0 = -3 & -4 dB IE (inner)/IA (outer) 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.00.0 IA (inner)/IE (outer) 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 Figure 8: EXIT chart curves and decoding trajectories for Rate-4/5 CSM (Mapping-I) scheme. Complexity 7 Rate-2/5 Outer CSM Rate-5/8 Inner RSC Eb/N0 = -6 & -6.5 dB IE (inner)/IA (outer) 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.00.0 IA (inner)/IE (outer) 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 Figure 9: EXIT chart curves and decoding trajectories for Rate-4/10 CSM (Mapping-II) scheme. Rate-5/6 Outer CSM Rate-3/10 Inner RSC Eb/N0 = -3 & -4 dB IE (inner)/IA (outer) 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.00.0 IA (inner)/IE (outer) 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 Figure 10: EXIT chart curves and decoding trajectories for Rate-5/6 CSM (Mapping-I) scheme. 8Complexity 3. Proposed System Overview Figure 1 depicts the block diagram of our proposed system based on the CSM scheme with DSTS-SP transmission mechanism and utilizing iterative JSCD approach at the receiver. The information bits are first allowed to the H.264 source encoder, transforming the data into squeezed form apt for transmission with minimal essential bandwidth. The compressed stream xkis DeMUltipleXed (DeMUX) to generate three different partitions. The technique of Data Partitioning (DP) is incorporated to assign specific important parameters and coding elements to partitions A, B, and C. The partitions are concatenated into single stream xi. There exists a certain level of unreliability in xi, mainly due to the concomitants of compression. To vouch for the desired reliability, CSM encoding is introduced as channel coding technique. CSM encoder transforms the video stream xiinto xi ′. The bit-interleaver Uassists in boosting the achievable performance of iterative scheme. There is a direct linkage between the degree of statistical independence associated with an interleaver and the number of interleaving bits [39]. The only hitch in longer interleaver is the delay it entails. To cope up with the delay, we concatenated the generated bits such that 99 Macroblocks (MBs) were within each frame. The interleaved stream xiis then RSC encoded with a specific rate to yield yi, which is SP modulated for propagation over Rayleigh fading channel. Finally, DSTS is applied to revamp the overall gain of system by embracing transmit diversity via two transmitting antennas (Txs). The decoding operation initiates with single receiving antenna (Rx1). Multiple receiving antennas can be utilized but we proceed with single antenna, merely for easier understanding of the concept. The received signal is allowed to a suboptimum DSTS decoder which continuously accepts interdependent signals from the Txs. The DSTS decoding presents a simple and handy approach for systems benefitting from the MIMO technique. SP demapper brings back the signal to its original frequency range and yields the L-values. The next stage constitutes an ISCD between the RSC and SBSD component decoders. The output from RSC decoder is interleaved to serve as a priori input to SBSD. The extrinsic output from SBSD is again deinterleaved U−1and fed to RSC decoder. The iterative process refines and gleans the best possible information exchange between component decoders. The iterative process yields L(􏽢 xm ′), which is passed through CSM decoder to generate the reconstructed stream 􏽢 xi. The stream is then deconcatenated for forwarding to MUltipleXer (MUX). Eventually, the reconstructed signal xi is processed by the source decoder to produce the decoded bits. Rate-5/12 Outer CSM Rate-3/5 Inner RSC Eb/N0 = -6 & -6.5 dB IE (inner)/IA (outer) 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.00.0 IA (inner)/IE (outer) 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 Figure 11: EXIT chart curves and decoding trajectories for Rate-5/12 CSM (Mapping-II) scheme. Complexity 9 [30] L. Lu, G. Y. Li, A. L. Swindlehurst, A. Ashikhmin, and R. Zhang, “An overview of massive MIMO: benefits and challenges,” IEEE journal of selected topics in signal processing, vol. 8, no. 5, pp. 742–758, 2014. 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