Uplink and downlink NOMA transmission using full-duplex UAV

Received August 20, 2020, accepted September 2, 2020, date of publication September 10, 2020, date of current version September 21, 2020. Digital Object Identifier 10.1109/ACCESS.2020.3023163 Uplink and Downlink NOMA Transmission Using Full-Duplex UAV DINH-THUAN DO 1, (Senior Member, IEEE), TU-TRINH THI NGUYEN 2, TU N. NGUYEN 3, (Senior Member, IEEE), XINGWANG LI 4, (Senior Member, IEEE), AND MIROSLAV VOZNAK 5, (Senior Member, IEEE) 1Wireless Communications Research Group, Faculty of Electrical & Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City 700000, Vietnam 2Faculty of Electronics Technology, Industrial University of Ho Chi Minh City, Ho Chi Minh City 700000, Vietnam 3Department of Computer Science, Purdue University Fort Wayne, Fort Wayne, IN 46805, USA 4School of Physical and Electronics Engineering, Henan Polytechnic University, Jiaozuo 454000, China 5Faculty of Electrical Engineering and Computer Science, Technical University of Ostrava, 70833 Ostrava, Czech Republic Corresponding author: Dinh-Thuan Do ([email protected]) This work was supported by the Czech Ministry of Education, Youth, and Sports, conducted at the VSB–Technical University of Ostrava, under Grant SP2020/65. ABSTRACT In this article, an unmanned aerial vehicle (UAV)-aided non-orthogonal multiple access (NOMA) network along with uplink (UL) and downlink (DL) transmissions is investigated. A group of sources intend to communicate with a group of destinations over Nakagami-mfading channels. Decodeand-Forward (DF) protocol is adopted at the relay (UAV plays the role of relay) while successive interference cancellation (SIC) is required at destinations (receivers) for signal detection, e.g., imperfect SIC (ipSIC) and perfect SIC (pSIC) are studied. The UAV relay benefits from the modes of full-duplex/half-duplex (FD/HD). We then derive analytical expressions of outage probability as main metric. Simulations are conducted to valid the analytical expressions. Moreover, the levels of the effectiveness of the FD mode and pSIC case are demonstrated through analysis and simulation and then compared with those of their counterparts. Numerical results are presented to validate the effectiveness of the proposed UAV-aided NOMA transmission strategies. INDEX TERMS Full-duplex, non-orthogonal multiple access, unmanned aerial vehicle, outage probability, throughput. I. INTRODUCTION In order to accommodate the explosive increase in data traffic of mobile devices, various transmission protocols in wireless networks and mobile networking techniques have been developed. Recently, technical evolution in the scope of massive connections has drawn great attention from the research community and industry. In order to support the tremendous demand for data traffic, a promising multiple access technique, namely non-orthogonal multiple access (NOMA) scheme. Such a technique benefits the mobile networking techniques by enabling massive connections for 5G mobile networks. The NOMA is known as exploiting the power domain into a new dimension to distinguish different users [1] and exhibits increased spectral efficiency over conventional orthogonal multiple access (OMA) scheme, e.g., The associate editor coordinating the review of this manuscript and approving it for publication was Mauro Fadda . TDMA/FDMA/CDMA schemes [3]. Unlike the well-known water-filling scheme, the system relying on NOMA allocates more power to users under weaker channel condition and thus maintains user fairness [4]. As a result, the NOMA system achieves larger total throughput and low latency of both uplink and downlink as compared to the OMA scheme [5], [6]. Such features of the NOMA make itself an attractive option for 5G networks and a serious competitor to other well-known schemes such as the orthogonal frequency division multiple access (OFDMA) [8]. Following the principle of the NOMA scheme, a large number of users can simultaneously access the same channel by employing superposition coding at the transmitter and successive interference cancellation (SIC) at the receiver. To exploit the benefit from spatial diversity, cooperative relaying has been explored. In general, relay network where the transmitter and receiver are interconnected by means of relay node has been introduced as an efficient VOLUME 8, 2020 This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ 164347 D.-T. Do et al.: UL and DL NOMA Transmission Using FD UAV way to extend the coverage and to overcome channel fading and path loss. The relay network can be classified into non-regenerative network and regenerative network. The relay of non-regenerative network operates in non-regenerative mode and the amplify-and-forward (AF) mode belongs to the non-regenerative mode. On the other hand, the relay of regenerative network regenerates the received signal by decoding followed by forwarding. The decode-and-forward (DF) mode is known as a regenerative mode, i.e. firstly decodes received signal and then re-transmits decoded signal to the destination. Lately, various works on the performance of cooperative relay network combined with the NOMA scheme have appeared in the literature [9]–[14]. Spatial multiplexing in cooperative relay network combined with the NOMA is presented in [15] to improve the spectral efficiency. To gain an insight into the successive interference cancellation (SIC), a relay network relying on NOMA has been studied in [16] and the important metrics such as ergodic sum rate with two SIC scenarios has been considered in [16]. A. RELATED WORK The full-duplex (FD) communication mode in relay networks [17], [18] is important for future wireless networks. By allowing simultaneous transmission and reception on the same frequency channel, the FD communication mode can enhance the spectral efficiency of wireless networks. However, because of self-interference, significant performance degradation can incur and due to imperfect RF chain isolation elimination of the signal leakage becomes challenging. The concept of relay network applied to the NOMA scheme leads to form cooperative NOMA scheme, where the near users that are close to the base station (BS) and thus in better channel conditions are used as relays to help the far users in poor channel conditions. Due to spectral efficiency of the FD communication mode and the flexibility in re-configuring relay networks, the FD communication can be in-cooperated with NOMA [19]. Recently, NOMA has been applied in the air-to-ground (A2G) communication system to improve the system performance [19]. For UAV-assisted mobile communication systems, UAV can serve as an aerial BS to offload part of the traffic from a heavy-crowded multiple access cellular network to improve the quality of service (QoS). Moreover, UAV can act as a flying cellular-connected user equipment (UE) such as cargo delivery applications or even a mobile relay to transfer data between two widely-separated end-points [20]. Regarding to multi-antenna UAV, a general multiple input multiple output (MIMO) NOMA UAV-aided network was proposed in [21]. They derived formula of the sum rate and outage performance of UAV-assisted network. Authors in [22] further considered the physical limitations of the antenna array and introduce the beam scanning to enhance the sum rate performance. In a cellular-connected UAV network, the concurrent uplink is enabled for transmission between the aerial user equipment (AUE) and a terrestrial user equipment (TUE) by implementing NOMA [23]. They considered the rate coverage probability which is known as the probability that the achievable rate of both the AUE and TUE exceeds the expected rates. In [24], NOMA multi-way relaying networks are studied to allow multiple terrestrial users exchange their mutual information via an AF-based UAV relay. The authors derived the analytical expressions for the achievable sum-rate. A model of dual-diversity receivers on UAVs is applied in a NOMA-aided UAV system and bivariate Rician shadowed fading channels are adopted [25]. Regarding promising applications of UAV, a UAV relay is more efficient in an emergency circumstance due to providing backhaul links to perform signals exchange. While a ground relay in a disaster area is likely destroyed in a harsh environment. As an advantage, a UAV relay with its high altitude is free from dangerous situations in the ground. Furthermore, a higher UAV altitude provides a better LoS propagation channel, which makes the AtG link benefit to current cellular systems and enhance the channel performance. Therefore, communication services are still be served in an emergency situation by enabling a UAV serving as an aerial relay. In particular, the authors in [26]- [33] investigated promising applications related NOMA, UAV, vehicle to everything (V2X). For example, [26] studied NOMA-enabled mobile relaying. In their study, to serve as a mobile decode-and-forward (DF) relay, a fixed-wing UAV flies in a circular trajectory to guarantee a reliable link. In [31], the authors considered a full duplex NOMA (FD-NOMA)-based decentralized V2X system model and to meet the requirements of massively connected devices, different quality of services (QoS). B. MOTIVATION AND OUR CONTRIBUTIONS In the foregoing scenarios of NOMA scheme, Rayleigh flat fading channel model is typically considered for performance evaluation. In our work, the performance of FD-NOMA scheme over general wireless channels is represented by exploiting Nakagami-mfading. It is pointed out that the Gaussian channel is a special case of the Nakagami channel with the fading parameter m=1/2 and the Rayleigh fading channel is a particular case with the fading parameter m=1. Indeed, very few works studied the performance of FD-NOMA scheme for UAV system over the Nakagamimfading channel. Although [26], [27] considered uplink downlink NOMA-assisted UAV system, however it is necessary to investigate the fundamental impact of communication channel on the performance of UAV system over Nakagamimchannel. Therefore, this article provides a general framework to design uplink downlink NOMA-assisted UAV in practice. This work also shows the relative performance of the FD-NOMA scheme over the HD-NOMA scheme and a comparison with result reported in [28] is further considered. The main contributions of this article are as follows •Different from [23]- [26], this study presents a FD UAV for uplink-downlink (UL-DL) in the scenario of NOMA (termed as FD UL-DL NOMA assisted UAV system), wherein two sources are able to communicate 164348 VOLUME 8, 2020 D.-T. Do et al.: UL and DL NOMA Transmission Using FD UAV FIGURE 1. System model of UL-DL UAV system relying on NOMA. simultaneously with their corresponding destinations via an FD-aided UAV over Nakagami-mfading channels. Unlike other published work dealing with the FD-NOMA scheme over specific type of communication channel such as the Rayleigh fading channel, our work provides generalized performance evaluation of the FD UL-DL NOMA-assisted UAV system in the presence of Nakagami-mchannels. •The closed-form expressions of outage probability for the HD-NOMA and FD-NOMA modes are derived. Since they are formulated in terms of various system parameters, the effect of each system parameter on the outage probability can be numerically evaluated. For instance, the effect of ipSIC on the outage probability can be evaluated to how the system works in practice. It is demonstrated in this work that the outage probability of the system relying on FD-NOMA mode is lower than that of HD-NOMA mode. •It can be concluded by analysis and simulation that the system relying on FD-NOMA scheme can achieve optimal outage performance with specific power allocation factor, self-interference level, and the transmit SNR at sources. Such optimal outage performance achieved with selected values of system parameters provides the guideline to design UAV system with highly efficient. •The derivation of asymptotic outage probability also provides an important evaluation to design such UAV system. Compared with OMA-assisted UAV system, the considered system exhibits more benefits and it becomes prominent candidate to implement in practice. •The comparison between our work with results reported in [28] in terms of outage probabilities provides an important insight on the behavior of these schemes according to specific evaluation criteria. Although a few work studied similar system model with our work, but such comparison is necessary to evaluate benefits of the considered UAV system. The remainder of this article is structured as follows. In Section II, the system model of the NOMA-assisted UAV system is introduced and UL-DL mechanism between two user pairs is mathematically described. In Section III, the closed-form expressions of outage probability are derived for the UAV system relying on HD-NOMA and FD-NOMA schemes operating over Nakagami-mchannels. In Section IV, the system model in HD mode and throughput performance in delay limited transmission mode is established. In Section V, we derive the approximated form of outage probability in the considered system. Section VI gives simulation results and corresponding performance analysis, followed by conclusions and future directions in Section VII. II. SYSTEM MODEL A. SYSTEM ARCHITECTURE Consider a scenario where two sources transmit signals to two different destinations via an intermediate UAV relay, which is shown as Fig. 1. In this scenario, the UAV operates as a relay to assist a group of transmitters which are distributed in one place intending to communicate with their corresponding destinations distributed in another place.1In this case, poor channel conditions and/or physical obstructions are the main reasons for the lack of direct transmission between sources and destinations. Considering an ideal situation, a perfect Decode and Forward (DF) mode is required to exactly decode the signal at the UAV. Now, the question is: how can a common relay help in multiple access for the UL and DL? To answer this difficult question, the following system and channel models are proposed in this study. First, a DF scheme and FD mode are jointly deployed at a UAV that performs the role of communication link serving two source-destination pairs, as in Fig. 1. The first 1As previous work [26], [27], we limit of our consideration on the two pairs of users. Similar analysis can be achieved for the case of high number of users. VOLUME 8, 2020 164349 D.-T. Do et al.: UL and DL NOMA Transmission Using FD UAV group contains U1,U3while U2,U4belong to the second group; these users employ the NOMA mechanism. It should be noted that U1,U2transmit their signals simultaneously to the distant users, i.e. U3,U4, but each dual-hop transmission corresponds to UL (first hop) and DL (second hop). In this scenario, the signal processing procedure occurs in two hops of the relaying network corresponding the two time slots. To handle uplink transmission for such a NOMA-assisted UAV system, both source nodes U1,U2 simultaneously transmit signals x1,x2, respectively, in the first hop; this is performed during the considered first time slot. We denote h1,h2as two channels that serve the uplink NOMA. Transmission from the UAV to the destination in the second hop is considered as downlink and these channels are denoted by g1,g2. It is assumed these channel follow the general Nakagami-mdistribution [24]. In the FD scenario, to represent residual self-interference, we denote fas a self-interference channel among transmit/receive antenna pairs equipped at the UAV. Regarding the ipSIC, interference channels are denoted as kr,k2. In the first phase, the allocated power factors are υ1P, υ2Pso as to distinguish power levels for the two symbols, i.e. x1,x2, corresponding to separated services for the first phase; υ3PR, υ4PR are re-assigned as allocated power for the two symbols in the second transmission phase, where υ1, υ2, υ3, υ4are the power allocation coefficients (PACs); finally, P,PRin this consideration are the total transmit power at the transmitters and the relay, respectively. Concurrently, the superimposed signal √PRυ3x1+√PRυ4x2is re-transmitted by the relay to U3,U4. It is worth noting that such a UAV can operate in FD mode which leads to processing delay with small time epoch. The constraint of power allocation fractions in NOMA are υ1+υ2=1, υ3+υ4=1 and their assignments have significant impacts on system performance [27]. Relying on NOMA, SIC is required at both UAV and destination to detect remaining signals; the two cases need to be considered carefully, i.e. ipSIC and pSIC. For positioning of UAV and NOMA users, we consider three dimensional Cartesian coordinates (x,y,z). First, we present the location of the sources U1,U2at U1−xU1,0,0,U2−xU2,0,0 with respect to the origin. The locations of destinations U3, U4are U3xU3,0,0,U4xU4,0,0, respectively. The central location of the circular trajectory of UAV (R) with radius rand altitude his at coordinates O0(0,h,0). Considering 8as the angle of the circle of UAV location with respect to the x-axis, we can readily represent the location of Rat Rrsin π 2−8,h,cos π 2−8. Based on the analysis, we can achieve the Euclidean distances from U1,U2to Rand from Rto U3,U4, are given respectively as dU1R=qr2+h2+x2 U1+2rxU1sin π 2−8, dU2R=qr2+h2+x2 U2+2rxU2sin π 2−8,dRU3= qr2+h2+x2 U3−2rxU3sin π 2−8,and dRU4= qr2+h2+x2 U4−2rxU4sin π 2−8. B. SIGNAL-TO-NOISE RATIO (SNR) CALCULATION We first consider FD mode for UL NOMA, x1is first decoded at the UAV relay to obtain a better channel condition. Then, x1is considered to be noise, and indicates poor channel condition..2The received signal at UAV relay can be given as yR=υ1pPSh1x1+υ2pPSh2x2+ηR,(1) where ηRis AWGN noise term, σ2 0is assumed to be noise variance for all noise terms. Therefore, the signal to interference plus noise ratio (SINR) to detect x1at relay can be formulated similar to [25] and it is given as γR←1=υ1ρS|h1|2 υ2ρS|h2|2+ρR|f|2+1,(2) where ρS 1 =Pσ2 0is the transmit signal to noise ratio (SNR) of source. Similarly, in HD mode, the SINR is rewritten as γHD R←1=υ1ρS|h1|2 υ2ρS|h2|2+1,(3) At the UAV, to detect signal x2, the operation of SIC is required; it is then carried out at the UAV to help in detection. In a pSIC situation, the SNR at the relay node to decode x2is determined in FD mode by γpSIC R←2=υ2ρS|h2|2 ρR|f|2+1.(4) In HD mode, the SNR is expressed by γpSIC,HD R←2=υ2ρS|h2|2.(5) When ipSIC occurs at the considered UAV system, these expressions can be recomputed respectively as γipSIC R←2=υ2ρS|h2|2 υ1ρS|kr|2+ρR|f|2+1,(6) and γipSIC,HD R←2=υ2ρS|h2|2 υ1ρS|kr|2+1,(7) where kr∼0mkr, βkris an interference term related to ipSIC; ρR 1 =PRσ2 0is the transmit SNR at the UAV. The received signal at destinations U3and U4can be expressed as follow yUn=υ3pPRg1x1+υ4pPRg2x2+ηUn.(8) Regarding the second hop transmission, the destination intends to decode its own data and hence the SINR can be achieved at destination U3by γU3=υ3ρR|g1|2 υ4ρR|g1|2+1.(9) 2Similar as recent work, the synchronization technique to transmit x1,x2 from two sources is beyond the scope of our paper [27]. 164350 VOLUME 8, 2020 D.-T. Do et al.: UL and DL NOMA Transmission Using FD UAV However, signal of destination U3is considered as noise at destination U4which intends to eliminate noise before detecting its own signal; hence, SINR can be achieved at destination U4as γU4←1=υ3ρR|g2|2 υ4ρR|g2|2+1.(10) Additionally, destination U4can decode its information after successful extraction of signal at U3data and application of SIC. Then, SNR and SINR at the second destination in the two cases including pSIC and impSIC, are given respectively as γpSIC U4=υ4ρR|g2|2,(11) and γipSIC U4=υ4ρR|g2|2 υ3ρR|k2|2+1.(12) III. OUTAGE PERFORMANCE IN FULL-DUPLEX MODE As an important metric, this section presents outage probability in a system containing links over an independent Nakagami-mfading channel. To evaluate system performance, our goal is to present outage behavior and main results in terms of analytical derivations. It should be mentioned that the outage probability of an ipSIC is the worst case that can be provided. In other words, the different scenarios in such NOMA including HD, FD, pSIC and ipSIC are determined carefully. A. OUTAGE PROBABILITY OF THE FIRST USER PAIR The considered system is one that classifies different users with corresponding required quality of service (QoS). In this case, γ1 0is assumed to be the predefined SINR thresholds of destination U3. Recalling that channel is denoted by |z|2. Since this channel follows a Gamma distribution, we present the probability density function (PDF) and the CDF of |z|2respectively as f|z|2(x)=xmz−1 0(mz)βmz z exp −x βz,(13) and F|z|2(x)=1−1 0(mz)0mz,x βz =1−exp −x βzmz−1 X n=0 xn n!βn z ,(14) where fZ(.)and FZ(.)represent the probability distribution function (PDF) and the cumulative distribution function of random variables (RVs), Z, respectively. The variable βzis defined as βz 1 =λzmzwith λzand mzrepresenting the mean and integer fading factor. The symbol 0(.)stands for the gamma function. The first user pair is evaluated according to the outage probability. In this type of NOMA-assisted UAV system, an outage event for the first user pair is explained as: i) relay cannot decode x1correctly; ii) information x1cannot be detected by U3. In particular, be denoting γ1 0=2R1−1 is required SINR threshold along with target rate R1, the exact outage probability of the first user pair is given by OPFD 1=Pr γR←1< γ 1 0∪γU3< γ 1 0 =1−Pr γR←1≥γ1 0, γU3≥γ1 0 =1−Pr |h1|2≥γ1 0 υ1ρSυ2ρS|h2|2+ρR|f|2+1! ×Pr |g1|2≥γ1 0 υ3−υ4γ1 0ρR!,(15) where Pr (.)is the outage probability function. We first introduce a proposition to calculate the outage performance as follow Proposition 1: The closed-form expression of first user pair in terms of outage probability can be given by OPFD 1=1−Pr |h1|2≥γ1 0 υ1ρSυ2ρS|h2|2+ρR|f|2+1! × 1−F|g1|2 γ1 0 υ3−υ4γ1 0ρR!! =1−exp −γ1 0 υ1ρSβh1! × mh1−1 X n=0 n X n1=0 n1 X n2=0n n1n1 n21 n!βn h1 × γ1 0 υ1ρS!n (ρR)n2(υ2ρS)n1−n2 ×1 0mfβmf f 1 0mh2βmh2 h2 × γ1 0ρR υ1ρSβh1+1 βf!−(n2+mf) 0n2+mf × γ1 0υ2 υ1βh1+1 βh2!−n1+n2−mh2 ×0n1−n2+mh2 ×exp −γ1 0 υ3−υ4γ1 0ρRβg1! × mg1−1 X n3=0γ1 0n3 n3!βn3 g1υ3−υ4γ1 0n3ρn3 R .(16) Proof: See Appendix A. B. OUTAGE PROBABILITY OF THE SECOND USER PAIR WITH ipSIC In this case, γ2 0is assumed to be the predefined SINR thresholds of destination U4. In a similar way, the second user pair VOLUME 8, 2020 164351 D.-T. Do et al.: UL and DL NOMA Transmission Using FD UAV exhibits outage behavior, as follow OPipSIC,FD 2=Pr γR←1< γ 1 0∪γipSIC R←2< γ 2 0 ∪γU4←1< γ 1 0∪γipSIC U4< γ 2 0! =1−Pr γR←1≥γ1 0, γ ipSIC R←2≥γ2 0 | {z } 91 ×Pr γU4←1≥γ1 0, γ ipSIC U4≥γ2 0 | {z } 92 ,(17) where γ2 0=2R2−1. Proposition 2: The closed-form expression of the second user pair in term of outage probability can be computed by OPipSIC,FD 2=1−91×92,(18) in which 91, 92are calculated as shown at the bottom of the page, in (19) and (20) respectively. Proof: See Appendix B. C. OUTAGE PROBABILITY OF THE SECOND USER PAIR IN pSIC CASE In this section, we consider a NOMA-assisted UAV network under the existence of pSIC. In principle, SIC or multilevel decoding can be carried out at the receiver side to extract superimposed messages transmitted from source nodes. In this situation, SIC can be carried out for the same user pair in both the uplink and downlink, and thus the total system capacity can be maximized. Therefore, to ensure optimal communications, it is reasonable to consider the best-case scenario of SIC, in which the residual interference is extremely small. In such a case, the expressions of outage probability at the second user pair can be rewritten as OPpSIC,FD 2=Pr γR←1< γ 1 0∪γpSIC R←2< γ 2 0 ∪γU4←1< γ 1 0∪γpSIC U4< γ 2 0! =1−Pr γR←1≥γ1 0, γ pSIC R←2≥γ2 0 | {z } 93 ×Pr γU4←1≥γ1 0, γ pSIC U4≥γ2 0 | {z } 94 .(21) Next, we provide an extra proposition as below Proposition 3: Regarding the outage probability, the expression of the second user pair in the pSIC case can be expressed in closed-form by OPpSIC,FD 2=1−93×94,(22) 91=exp −γ1 0 υ1ρSβh1!mh1−1 X n=0 n X n1=0 n1 X n2=0n n1n1 n21 n!βn h1 γ1 0 υ1ρS!n ×exp −υ2γ1 0γ2 0 υ1υ2ρSβh1−γ2 0 υ2ρSβh2−γ1 0 υ1ρsβh1!ρRn2(υ2ρS)n1−n2 ×1 0mh2βmh2 h2 γ1 0υ2 υ1βh1+1 βh2!−n1+n2−mh2n1−n2+mh2−1! × n1−n2+mh2−1 X n3=0 n3 X n4=0n3 n4n4 n51 n3! υ2γ1 0γ2 0 υ1υ2ρSβh1+γ2 0 υ2ρSβh2!n3 ×0n2+n4−n5+mf0n5+mkrρRn4−n5υ1n5ρSn5 ×1 0mfβmf f γ1 0ρR υ1ρSβh1+ γ1 0υ2 υ1βh1+1 βh2!γ2 0ρR υ2ρS+1 βf!−(n2+n4−n5+mf) ×1 0mkrβmkr kr γ1 0υ2 υ1βh1+1 βh2!γ2 0υ1 υ2+1 βkr!−(n5+mkr) ,(19) 92=exp −γ2 0 υ4ρRβg2!mg2−1 X n6=0 n6 X n7=0n6 n71 n6!βn6 g2 γ2 0 υ4ρR!n6 υn7 3ρn7 R ×1 0mk2βmk2 k2 γ2 0υ3 υ4βg2+1 βk2!−n7+mk20 n7+mk2,γ2 0υ3ϑ2 υ4βg2+ϑ2 βk2! +1 0mg20 mg2,γ1 0 υ3−γ1 0υ4ρRβg2!× 1−1 0mk20mk2,ϑ2 βk2!(20) 164352 VOLUME 8, 2020 D.-T. Do et al.: UL and DL NOMA Transmission Using FD UAV in which, 93=e−γ1 0 υ1ρSβh1−γ1 0υ2 υ1βh1+1 βh2γ2 0 υ2ρS × mh1−1 X n=0 n X n1=0 n1 X n2=0n n1n1 n21 n!βn h1 γ1 0 υ1ρS!n ×ρn2 R(υ2ρS)n1−n2n1−n2+mh2−1 ×1 0mh2βmh2 h2 γ1 0υ2 υ1βh1+1 βh2!−n1+n2−mh2 × n1−n2+mh2−1 X n3=0 n3 X n4=0n3 n41 n3! × γ1 0υ2 υ1βh1+1 βh2!γ2 0 υ2ρS!n3 ×ρn4 R 0mfβmf f 0n2+n4+mf × γ1 0ρR υ1ρsβh1+γ1 0γ2 0ρr υ1ρs+γ2 0ρR βh2υ2ρS+1 βf!−n2−n4−mf (23) and 94=1 0mg20 mg2,1 βg2 max γ1 0 υ3−γ1 0υ4ρR ,γ2 0 υ4ρR!! (24) Proof: See Appendix C. IV. OUTAGE PERFORMANCE IN HALF-DUPLEX MODE AND THROUGHPUT CONSIDERATION In this section, to enable comparison to the counterpart, HD mode is deployed in our proposed system. Without self-interference due to parallel operation of the dual-antenna at the relay, receivers are required to have lighter signal processing, and hence performance of HD mode still needs to be considered in terms of outage probability. Considering further metrics, throughput performance can be evaluated in two modes, i.e. FD and HD. A. OUTAGE PERFORMANCE IN HALF-DUPLEX MODE In this scenario, to examine the first main metric, we consider the outage probability. In particular, outage performance of the first user pair is expressed in HD mode by OPHD 1=Pr γHD R←1< γ 1 0,HD ∪γU3< γ 1 0,HD =1−Pr γHD R←1≥γ1 0,HD, γU3≥γ1 0,HD =1−Pr υ1ρS|h1|2 υ2ρS|h2|2+1≥γ1 0,HD! ×Pr υ3ρr|g1|2 υ4ρR|g1|2+1≥γ1 0,HD!,(25) where γ1 0,HD =22R1−1. After several manipulations, the following proposition can be introduced. Proposition 4: In HD mode, the outage probability for the first user pair can be formulated in closed-form as OPHD 1=1−e−γ1 0,HD υ1ρSβh1−γ1 0,HD υ3−υ4γ1 0,HDρRβg1 × mh1−1 X n=0 n X n1=0n n11 n! γ1 0,HD υ1ρSβh1!n ×υn1 2ρn1 S0n1+mh2 ×1 0mh2βmh2 h2 γ1 0,HDυ2 υ1βh1+1 βh2!−n1−mh2 × mg1−1 X n2=0 1 n2!  γ1 0,HD υ3−υ4γ1 0,HDβg1ρR  n2 . (26) Proof: See Appendix D. In case of ipSIC, outage probability can be expressed for the second user pair as OPipSIC,HD 2=Pr γHD R←1< γ 1 0,HD ∪γipSIC,HD R←2< γ 2 0,HD ∪γU4←1< γ 1 0,HD ∪γipSIC U4< γ 2 0,HD ! =1−Pr γHD R←1≥γ1 0,HD, γ ipSIC,HD R←2≥γ2 0,HD | {z } 95 ×Pr γU4←1≥γ1 0,HD, γ ipSIC U4≥γ2 0,HD | {z } 96 , (27) where γ2 0,HD =22R2−1. Therefore, the outage calculation motivates us to introduce the following proposition. Proposition 5: In terms of the ipSIC case in HD mode, the outage probability for the second user pair can be exactly computed as OPipSIC,HD 2=1−95×96,(28) in which, 95=e−γ1 0,HD υ1ρSβh1−ϑ1γ2 0,HD υ2ρS × mh1−1 X n=0 n X n1=0n n1υn1 2 n!ρn−n1 S γ1 0,HD υ1βh1!n ×0(τ2)ϑ1−τ2 0mh2βmh2 h2 τ2−1 X n2=0 n2 X n3=0n2 n31 n2! × ϑ1γ2 0,HD υ2ρS!n2 υn3 1ρn3 S0n3+mkr ×1 0mkrβmkr kr υ1ϑ1γ2 0,HD υ2+1 βkr!−n3−mkr (29) VOLUME 8, 2020 164353 D.-T. Do et al.: UL and DL NOMA Transmission Using FD UAV and 96=e−γ2 0,HD υ4ρRβg2 mg2−1 X n6=0 n6 X n7=0n6 n71 n6!βn6 g2 ×υn7 3ρn7 R γ2 0,HD υ4ρR!n6 ×1 0mk2βmk2 k2 γ2 0,HDυ3 υ4βg2+1 βk2!−n7−mk2 ×0 n7+mk2, γ2 0,HDυ3 υ4βg2+1 βk2!ϑ2! +1 0mg20 mg2,γ1 0,HD υ3−γ1 0,HDυ4ρRβg2  × 1−1 0mk20mk2,ϑ2 βk2!(30) Proof: See Appendix E. In a similar way, the outage probability for the second user pair under the ipSIC case can be obtained as follow. Proposition 6: In HD mode and ipSIC case, the second user pair can be computed in term of outage behavior exactly as OPpSIC,HD 2=Pr γHD R←1< γ 1 0,HD ∪γpSIC,HD R←2< γ 2 0,HD ∪γU4←1< γ 1 0,HD ∪γpSIC U4< γ 2 0,HD ! =1−Pr γHD R←1≥γ1 0,HD, γ pSIC,HD R←2≥γ2 0,HD | {z } 97 ×Pr γU4←1≥γ1 0,HD, γ pSIC U4≥γ2 0,HD | {z } 98 , (31) where 97=e−γ1 0,HD υ1ρSβh1 mh1−1 X n=0 n X n1=0n n1υn1 2ρn1 S n!βn h1 γ1 0,HD υ1ρS!n ×1 0mh2βmh2 h2 γ1 0,HDυ2 υ1βh1+1 βh2!−n1−mh2 ×0 n1+mh2, γ1 0,HDυ2 υ1βh1+1 βh2!γ2 0,HD υ2ρS!,(32) and 98=1 0mg20      mg2,1 βg2 ×max   γ1 0,HD υ3−γ1 0,HDυ4ρR ,γ2 0,HD υ4ρR        . (33) Similarly, the implicit outage derivation in this proposition can be obtained as for the previously computed position. Due to the simplicity of the analysis, we do not present it here. B. THROUGHPUT PERFORMANCE IN IN DELAY-LIMITED TRANSMISSION In this scenario, we consider the throughput in delay-limited transmission for FD and HD NOMA, respectively. In the first scenario related to FD, the throughput of the first user pair corresponding the fixed bit rate R1can be computed by AFD 1=1−OPFD 1×R1.(34) Similarly, the throughput of the second user pair corresponding to fixed bit rate R2in case of ipSIC will become AipSIC,FD 2=1−OPipSIC,FD 2×R2.(35) The sum of the throughput of the system in the case of ipSIC can be computed by AipSIC,FD sum =1−OPFD 1×R1+1−OPipSIC,FD 2×R2. (36) In addition, the throughput of the second user pair corresponding to fixed bit rate R2in the case of pSIC is given by ApSIC,FD 2=1−OPpSIC,FD 2×R2.(37) The sum of the throughput of the system in the case of pSIC at FD mode can be computed by ApSIC,FD sum =1−OPFD 1×R1+1−OPpSIC,FD 2×R2. (38) In HD mode, throughput performance can be obtained in three cases, as follows AHD 1=1−OPHD 1×R1,(39) AipSIC,HD 2=1−OPipSIC,HD 2×R2.(40) In the case of ipSIC, the sum throughput of the system can be formulated as AipSIC,HD sum =1−OPHD 1×R1+1−OPipSIC,HD 2×R2, (41) and ApSIC,HD 2=1−OPpSIC,HD 2×R2.(42) In HD mode, the sum throughput of the system in the case of pSIC can be obtained as ApSIC,HD sum =1−OPHD 1×R1+1−OPpSIC,HD 2×R2. (43) V. APPROXIMATION ANALYSIS Based on the analytical results in (16), (18), (22), (26), (28) and (31), when ρ→ ∞, the asymptotic outage probabilities of the first and second user pairs for ipSIC/pSIC with e−x≈ 1−xare given as (44)–(47), shown at the bottom of the next page, (48) and (49), as shown at the bottom of the page 10. In this section, we denote τ21 =n+mh2,4= υ1γ2 0 υ2γ1 0υ2 υ1βh1+1 βh2+1 βkr. 164354 VOLUME 8, 2020 D.-T. Do et al.: UL and DL NOMA Transmission Using FD UAV VI. NUMERICAL AND SIMULATION RESULTS Simulations are accomplished for system performance evaluation in MATLAB to obtain impacts of parameters on FD UL-DL NOMA-assisted UAV system. We provide both analytical and simulation results. The simulation parameters, unless otherwise specified, are βh1=dU1R−α , βh2=dU2R−α , βg1=dRU3−α, OPFD 1,asym =1−Pr |h1|2≥γ1 0 υ1υ2|h2|2+|f|2! 1−F|g1|2 1 υ3−υ4γ1 0!! = mh1−1 X n=0 n X n1=0n n1mg1−1 X n2=0 υ2n−n1 n!βn h1 0n1+mf0n−n1+mh2 0mf0mh2βmf fβmh2 h2 × γ1 0 υ1!n γ1 0 υ1βh1+1 βf!−n1−mf γ1 0υ2 υ1βh1+1 βh2!−n+n1−mh2 ×1 n2!βn2 g1 1−1 υ3−υ4γ1 0βg1! 1 υ3−υ4γ1 0!n2 ,(44) OPipSIC,FD 2,asym =1− mh1−1 X n=0 n X n1=0n n1τ1−1 X n2=0 n2 X n3=0n2 n3υn−n1 2 n!βn h1 γ1 0 υ1!n ×υ1n3 n2! ϑ1γ2 0 υ2!n2n−n1+mh2−1!ϑ1−τ1 0mh2βmh2 h20mkr0mfβmkr krβmf f × ϑ1γ2 0 υ2+γ1 0 υ1βh1+1 βf!−n2+n3−n1−mf 1 βkr+ϑ1γ2 0υ1 υ2!−n3−mkr ×n2−n3+n1+mf−1!n3+mkr−1! ×  1−1 υ3−υ4γ1 0βg2!mg2−1 X n4=0 1 n4!βn4 g2 1 υ3−υ4γ1 0!n4 × 1−1 0mk20 mk2,υ4 γ1 0υ3βk2υ3−γ1 0υ4!! + mg2−1 X n5=0 1 n5!βn5 g2 γ1 0υ3 υ4!n5 γ1 0υ3 υ4βg2!−n5−1 0 n5+1,1 βg2υ3−γ1 0υ4! ,(45) OPpSIC,FD 2,asym =1− mh1−1 X n=0 n X n1=0n n1τ1−1 X n2=0 n2 X n3=0n2 n3mg2−1 X n4=0 υn−n1 2 n!n2!βn h1 γ1 0 υ1!n ϑ1γ2 0 υ2!n2 ×ϑ1−τ1n−n1+mh2−1!0n1+n3+mf 0mf0mh2βmf fβmh2 h2 γ1 0 υ1βh1+1 βf+ϑ1γ2 0 υ2!−n1−n3−mf ×1 n4!βn4 g2 1−1 υ3−υ4γ1 0βg2! 1 υ3−υ4γ1 0!n4 ,(46) OPHD 1,asym =1−Pr |h1|2≥υ2γ1 0 υ1|h2|2! 1−F|g1|2 1 υ3−υ4γ1 0!! = mh1−1 X n=0 mg1−1 X n1=0 0n+mh2 n!n1!0mh2βn h1βn1 g1βmh2 h2 υ2γ1 0 υ1!n × 1−1 υ3−υ4γ1 0βg1! 1 υ3−υ4γ1 0!n1 γ1 0υ2 υ1βh1+1 βh2!−n−mh2 (47) VOLUME 8, 2020 164355 D.-T. Do et al.: UL and DL NOMA Transmission Using FD UAV To address final outage event, it need be computed these outage expressions as follows $3=E|h1|2(1−F|h1|2 γ1 0,HD υ1ρSυ2ρS|h2|2+1!), (D.2) where E{.}indicates the expectation operator. After the implementation of the calculation, we have $3(y) =e−γ1 0,HD υ1ρSβh1e−γ1 0,HDυ2ρSy υ1ρsβh1 × mh1−1 X n=0 n X n1=0n n11 n! γ1 0,HD υ1ρSβh1!n υn1 2ρn1 Syn1.(D.3) Then, we have following result as $3 =e−γ1 0,HD υ1ρSβh1 mh1−1 X n=0 n X n1=0n n11 n! γ1 0,HD υ1ρSβh1!n ×υn1 2ρn1 S0n1+mh2 0mh2βmh2 h2 γ1 0,HDυ2 υ1βh1+1 βh2!−n1−mh2 , (D.4) and $4=F|g1|2 γ1 0,HD υ3−υ4γ1 0ρR! =1−e−γ1 0,HD υ3−υ4γ1 0,HDρRβg1 × mg1−1 X n3=0 1 n3!βn3 g1  γ1 0,HD υ3−υ4γ1 0,HDρR  n3 .(D.5) Plugging above values of (D.4) and (D.5) into (D.1) we obtain the final formula. This is end of proof. APPENDIX E PROOF OF PROPOSITION 5 After simple manipulations, we have following equation as 95=Pr υ1ρS|h1|2 υ2ρS|h2|2+1≥γ1 0,HD, υ2ρS|h2|2 υ1ρS|kr|2+1≥γ2 0,HD!.(E.1) After placing the following variables y=|h2|2,z=|kr|2and performing calculations, we have 95= ∞ Z γ2 0,HD υ2ρS(υ1ρSz+1) 1(y)fy(y)dy ∞ Z0 fz(z)dz,(E.2) with 1(y)=1−F|h1|2 γ1 0,HD υ1ρS (υ2ρSy+1)! =e−γ1 0,HD(υ2ρSy+1) υ1ρSβh1 mh1−1 X n=0 1 n!βn h1 × γ1 0,HD υ1ρS!n (υ2ρSy+1)n = mh1−1 X n=0 n X n1=0n n11 n! γ1 0,HD υ1ρSβh1!n ×υn1 2ρn1 Se−γ1 0,HD υ1ρSβh1e−γ1 0,HDυ2 υ1βh1yyn1.(E.3) In this case, the last step can be performed by using trinomial expansion [ [34], eq. (1.111)]. Plugging (E.3) into (E.2), and after making some manipulations with the help of [ [34], 93=Pr γR←1≥γ1 0, γ pSIC R←2≥γ2 0 = mh1−1 X n=0 n X n1=0 n1 X n2=0n n1n1 n2υn1−n2 2ρn1−n2 Sρn2 R n!βn h1 γ1 0 υ1ρs!n ×e−υ2γ1 0γ2 0 υ1υ2ρSβh1−γ1 0 υ1ρSβh1−γ2 0 υ2ρSβh2 0mh2βmh2 h2 γ1 0υ2 υ1βh1+1 βh2!−n1+n2−mh2n1−n2+mh2−1! × n1−n2+mh2−1 X n3=0 n3 X n4=0n3 n4ρRn4 n3!0mfβmf f υ2γ1 0γ2 0 υ1υ2ρSβh1+γ2 0 υ2ρSβh2!n3 × γ1 0ρR υ1ρSβh1+ γ1 0υ2 υ1+1 βh2!γ2 0ρR υ2ρS+1 βf!−n2−n4−mf 0n2+n4+mf(C.1) 164362 VOLUME 8, 2020 D.-T. Do et al.: UL and DL NOMA Transmission Using FD UAV eq. (3.381.3), eq. (3.381.4)and eq. (8.352.2)], it is given by 95= mh1−1 X n=0 n X n1=0n n1υn1 2e−γ1 0,HD υ1ρSβh1 n!ρn−n1 S γ1 0,HD υ1βh1!n ×1 0mkrβmkr kr ∞ Z0 2(z)zmkr−1e−z βkrdz,(E.4) with 2(z)= ∞ Z γ2 0,HD υ2ρS(υ1ρSz+1) e−γ1 0,HDυ2 υ1βh1yyn1fy(y)dy =ϑ1−τ2 0mh2βmh2 h2 0 τ2,ϑ1γ2 0,HD υ2ρS (υ1ρSz+1)! =0(τ2)ϑ1−τ2 0mh2βmh2 h2 e−ϑ1γ2 0,HD υ2ρS τ2−1 X n2=0 n2 X n3=0n2 n31 n2! × ϑ1γ2 0,HD υ2ρS!n2 υn3 1ρn3 Se−ϑ1γ2 0,HDυ1z υ2zn3,(E.5) where τ21 =n1+mh2and ϑ11 =γ1 0,HDυ2 υ1βh1+1 βh2. Finally, replacing (E.5) into (E.4), we have 95=e−γ1 0,HD υ1ρSβh1 mh1−1 X n=0 n X n1=0n n1υn1 2 n!ρn−n1 S γ1 0,HD υ1βh1!n ×0(τ2)ϑ1−τ1e−ϑ1γ2 0,HD υ2ρS 0mh2βmh2 h2 τ2−1 X n2=0 n2 X n3=0n2 n31 n2! × ϑ1γ2 0,HD υ2ρS!n2υn3 1ρn3 S 0mkrβmkr kr ×0n3+mkr υ1ϑ1γ2 0,HD υ2+1 βkr!−n3−mkr . (E.6) In HD mode, because γU4←1and γipSIC U4are unchanged compared to the full duplex mode, we have 96=e−γ2 0,HD υ4ρRβg2 mg2−1 X n6=0 n6 X n7=0n6 n7υn7 3ρn7 R n6!βn6 g2 γ2 0,HD υ4ρR!n6 ×1 0mk2βmk2 k2 γ2 0,HDυ3 υ4βg2+1 βk2!−n7−mk2 ×0 n7+mk2,γ2 0,HDυ3ϑ2 υ4βg2+ϑ2 βk2! +1 0mg20 mg2,γ1 0,HD υ3ρRβg2−υ4ρRγ1 0,HDβg2! × 1−1 0mk20mk2,ϑ2 βk2!,(E.7) This is end of proof. REFERENCES [1] X. Li, J. Li, and L. Li, ‘‘Performance analysis of impaired SWIPT NOMA relaying networks over imperfect weibull channels,’’ IEEE Syst. J., vol. 14, no. 1, pp. 669–672, Mar. 2020. [2] X. Li, M. Liu, D. Deng, J. Li, C. Deng, and Q. Yu, ‘‘Power beacon assisted wireless power cooperative relaying using NOMA with hardware impairments and imperfect CSI,’’ AEU-Int. J. Electron. Commun., vol. 108, pp. 275–286, Aug. 2019. [3] L. Zhang, M. Xiao, G. Wu, M. Alam, Y.-C. Liang, and S. Li, ‘‘A survey of advanced techniques for spectrum sharing in 5G networks,’’ IEEE Wireless Commun., vol. 24, no. 5, pp. 44–51, Oct. 2017. [4] D.-T. Do, A.-T. Le, and B. M. Lee, ‘‘NOMA in cooperative underlay cognitive radio networks under imperfect SIC,’’ IEEE Access, vol. 8, pp. 86180–86195, 2020. [5] D.-T. Do and M.-S. Van Nguyen, ‘‘Device-to-device transmission modes in NOMA network with and without wireless power transfer,’’ Comput. Commun., vol. 139, pp. 67–77, May 2019. [6] D.-T. Do and A.-T. Le, ‘‘NOMA based cognitive relaying: Transceiver hardware impairments, relay selection policies and outage performance comparison,’’ Comput. Commun., vol. 146, pp. 144–154, Oct. 2019. [7] T.-L. Nguyen and D.-T. Do, ‘‘Power allocation schemes for wireless powered NOMA systems with imperfect CSI: An application in multiple antenna-based relay,’’ Int. J. Commun. Syst., vol. 31, no. 15, p. e3789, Oct. 2018. [8] Z. Wei, J. Yuan, D. W. K. Ng, M. Elkashlan, and Z. Ding, ‘‘A survey of downlink non-orthogonal multiple access for 5G wireless communication networks,’’ 2016, arXiv:1609.01856. [Online]. Available: http://arxiv.org/abs/1609.01856 [9] F. Wei, T. Zhou, T. Xu, and H. Hu, ‘‘Modeling and analysis of two-way relay networks: A joint mechanism using NOMA and network coding,’’ IEEE Access, vol. 7, pp. 152679–152689, 2019. [10] W. Zhao, R. She, and H. Bao, ‘‘Security energy efficiency maximization for two-way relay assisted cognitive radio NOMA network with selfinterference harvesting,’’ IEEE Access, vol. 7, pp. 74401–74411, 2019. [11] W. Zhao, R. She, and H. Bao, ‘‘Energy efficiency maximization for twoway relay assisted CR-NOMA system based on SWIPT,’’ IEEE Access, vol. 7, pp. 72062–72071, 2019. [12] D.-T. Do, A.-T. Le, and A. B. M. Lee, ‘‘On performance analysis of underlay cognitive radio-aware hybrid OMA/NOMA networks with imperfect CSI,’’ Electronics, vol. 8, no. 7, p. 819, Jul. 2019. [13] Z. Ding, M. Peng, and H. V. Poor, ‘‘Cooperative non-orthogonal multiple access in 5G systems,’’ IEEE Commun. Lett., vol. 19, no. 8, pp. 1462–1465, Aug. 2015. [14] M. Xu, F. Ji, M. Wen, and W. Duan, ‘‘Novel receiver design for the cooperative relaying system with non-orthogonal multiple access,’’ IEEE Commun. Lett., vol. 20, no. 8, pp. 1679–1682, Aug. 2016. [15] J.-B. Kim and I.-H. Lee, ‘‘Capacity analysis of cooperative relaying systems using non-orthogonal multiple access,’’ IEEE Commun. Lett., vol. 19, no. 11, pp. 1949–1952, Nov. 2015. [16] M. F. Kader, M. B. Shahab, and S. Y. Shin, ‘‘Exploiting non-orthogonal multiple access in cooperative relay sharing,’’ IEEE Commun. Lett., vol. 21, no. 5, pp. 1159–1162, May 2017. [17] X.-X. Nguyen and D.-T. Do, ‘‘Maximum harvested energy policy in fullduplex relaying networks with SWIPT,’’ Int. J. Commun. Syst., vol. 30, no. 17, p. e3359, Nov. 2017. [18] X.-X. Nguyen and D.-T. Do, ‘‘Optimal power allocation and throughput performance of full-duplex DF relaying networks with wireless power transfer-aware channel,’’ EURASIP J. Wireless Commun. Netw., vol. 2017, no. 1, p. 152, Dec. 2017. [19] M. F. Sohail, C. Y. Leow, and S. Won, ‘‘Non-orthogonal multiple access for unmanned aerial vehicle assisted communication,’’ IEEE Access, vol. 6, pp. 22716–22727, 2018. [20] Y. Zeng, R. Zhang, and T. J. Lim, ‘‘Wireless communications with unmanned aerial vehicles: Opportunities and challenges,’’ IEEE Commun. Mag., vol. 54, no. 5, pp. 36–42, May 2016. [21] T. Hou, Y. Liu, Z. Song, X. Sun, and Y. Chen, ‘‘Multiple antenna aided NOMA in UAV networks: A stochastic geometry approach,’’ IEEE Trans. Commun., vol. 67, no. 2, pp. 1031–1044, Feb. 2019. [22] N. Rupasinghe, Y. Yapıcı, İ. Güvenç, and Y. Kakishima, ‘‘Non-orthogonal multiple access for mmWave drone networks with limited feedback,’’ IEEE Trans. Commun., vol. 67, no. 1, pp. 762–777, Jan. 2019. VOLUME 8, 2020 164363 D.-T. Do et al.: UL and DL NOMA Transmission Using FD UAV [23] N. Senadhira, S. Durrani, X. Zhou, N. Yang, and M. Ding, ‘‘Uplink NOMA for cellular-connected UAV: Impact of UAV trajectories and altitude,’’ IEEE Trans. Commun., vol. 68, no. 8, pp. 5242–5258, Aug. 2020, doi: 10.1109/TCOMM.2020.2995373. [24] X. Li, Q. Wang, Y. Liu, T. A. Tsiftsis, Z. Ding, and A. Nallanathan, ‘‘UAVaided multi-way NOMA networks with residual hardware impairments,’’ IEEE Wireless Commun. Lett., vol. 9, no. 9, pp. 1538–1542, Sep. 2020, doi: 10.1109/LWC.2020.2996782. [25] T. Z. H. Ernest, A. S. Madhukumar, R. P. Sirigina, and A. K. Krishna, ‘‘NOMA-aided UAV communications over correlated rician shadowed fading channels,’’ IEEE Trans. Signal Process., vol. 68, pp. 3103–3116, 2020. [26] L. Wang, B. Hu, S. Chen, and J. Cui, ‘‘UAV-enabled reliable mobile relaying based on downlink NOMA,’’ IEEE Access, vol. 8, pp. 25237–25248, 2020. [27] X. Yue, Y. Liu, S. Kang, A. Nallanathan, and Y. Chen, ‘‘Modeling and analysis of two-way relay non-orthogonal multiple access systems,’’ IEEE Trans. Commun., vol. 66, no. 9, pp. 3784–3796, Sep. 2018. [28] M. F. Kader, S. Y. Shin, and V. C. M. Leung, ‘‘Full-duplex non-orthogonal multiple access in cooperative relay sharing for 5G systems,’’ IEEE Trans. Veh. Technol., vol. 67, no. 7, pp. 5831–5840, Jul. 2018. [29] Y. Saito, Y. Kishiyama, A. Benjebbour, T. Nakamura, A. Li, and K. Higuchi, ‘‘Non-orthogonal multiple access (NOMA) for cellular future radio access,’’ in Proc. IEEE 77th Veh. Technol. Conf. (VTC Spring), Jun. 2013, pp. 1–5. [30] D.-T. Do, M.-S. V. Nguyen, A.-T. Le, J. Zhang, and K. Rabie, ‘‘Joint FullDuplex and roadside unit selection for NOMA-enabled V2X communications: Ergodic rate performance,’’ IEEE Access, vol. 8, pp. 140348–140360, 2020. [31] D. Zhang, Y. Liu, L. Dai, A. K. Bashir, A. Nallanathan, and B. Shim, ‘‘Performance analysis of FD-NOMA-based decentralized V2X systems,’’ IEEE Trans. Commun., vol. 67, no. 7, pp. 5024–5036, Jul. 2019. [32] J. Montalban, P. Scopelliti, M. Fadda, E. Iradier, C. Desogus, P. Angueira, M. Murroni, and G. Araniti, ‘‘Multimedia multicast services in 5G networks: Subgrouping and non-orthogonal multiple access techniques,’’ IEEE Commun. Mag., vol. 56, no. 3, pp. 91–95, Mar. 2018. [33] P. K. Sharma and D. I. Kim, ‘‘UAV-enabled downlink wireless system with non-orthogonal multiple access,’’ in Proc. IEEE Globecom Workshops (GC Wkshps), Singapore, Dec. 2017, pp. 1–6. [34] I. S. Gradshteyn and I. M. Ryzhik, Table of Integrals, Series and Products, 7th ed. New York, NY, USA: Academic, 2007. DINH-THUAN DO (Senior Member, IEEE) received the B.S., M.Eng., and Ph.D. degrees from Vietnam National University (VNU-HCM), in 2003, 2007, and 2013, respectively, all in communications engineering. Prior to joining Ton Duc Thang University, he was a Senior Engineer with VinaPhone Mobile Network, from 2003 to 2009. He has published over 75 SCI/SCIE journal articles, one sole author book, and five book chapters. His research interests include signal processing in wireless communications networks, cooperative communications, non-orthogonal multiple access, full-duplex transmission, and energy harvesting. He was a recipient of the Golden Globe Award from the Vietnam Ministry of Science and Technology, in 2015 (Top 10 Excellent Young Scientists Nationwide). He has been serving as an Associate Editor for six journals, in which main journals are EURASIP Journal on Wireless Communications and Networking,Computer Communications (Elsevier), and the KSII Transactions on Internet and Information Systems. TU-TRINH THI NGUYEN received the B.Sc. degree in electrical-electronics engineering from the Industrial University of Ho Chi Minh City, Vietnam, in 2018. She intends to pursue her study in the Ph.D. degree. She is currently working with the WICOM Laboratory, which has lead by Dr. Thuan. Her research interests include signal processing in wireless communications networks, NOMA, and relaying networks. TU N. NGUYEN (Senior Member, IEEE) received the Ph.D. degree in electronic engineering from the National Kaohsiung University of Science and Technology (formerly, National Kaohsiung University of Applied Sciences), Kaohsiung, Taiwan, in 2016. He was a Postdoctoral Associate with the Department of Computer Science and Engineering, University of Minnesota-Twin Cities, in 2017. Prior to joining the University of Minnesota, he joined the Missouri University of Science and Technology as a Postdoctoral Researcher with the Intelligent Systems Center, in 2016. He is currently an Assistant Professor with the Department of Computer Science, Purdue University Fort Wayne, Fort Wayne, IN, USA. His research interests include design and analysis of algorithms, network science, cyber-physical systems, and cybersecurity. He was a Technical Program Committee Member for more than 70 premium conferences in the areas of network and communication, such as INFOCOM, Globecom, ICC, and RFID. He was the TPC Co-Chair of the NAFOSTED Conference on Information and Computer Science (NICS) 2019, SoftCOM (25th), and the EAI International Conference on Context-Aware Systems and Applications (ICCASA) 2017; the Publicity Chair of the International Conference on Awareness Science and Technology (iCAST) 2017 and BigDataSecurity 2017; and the Track Chair of ACT 2017. He has been an Associate Editor of IEEE ACCESS and the EURASIP Journal on Wireless Communications and Networking since 2017. He is also on the Editorial Board of the Cybersecurity journal, the Internet Technology Letters since 2017, the International Journal of Vehicle Information and Communication Systems since 2017, the International Journal of Intelligent Systems Design and Computing since 2017, and IET Wireless Sensor Systems since 2017. XINGWANG LI (Senior Member, IEEE) received the B.Sc. degree from Henan Polytechnic University, in 2007, the M.Sc. degree from the University of Electronic Science and Technology of China in 2010, and the Ph.D. degree from the Beijing University of Posts and Telecommunications, in 2015. From 2010 to 2012, he has worked with Comba Telecom Ltd., Guangzhou, China, as an Engineer. He was a Visiting Scholar with the State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, from 2016 to 2018. He spent one year as a Visiting Scholar with Queen’s University Belfast, Belfast, U.K., from 2017 to 2018. He is currently an Associate Professor with the School of Physics and Electronic Information Engineering, Henan Polytechnic University, Jiaozuo, China. His research interests include MIMO communication, cooperative communication, hardware constrained communication, non-orthogonal amultiple access, physical layer security, unmanned aerial vehicles, and the Internet of Things. He has served as a TPC member for IEEE/CIC International Conference on Communications in China (ICCC’2019) and IEEE Global Communications Conference 2018 (Globecom’18). He is currently an Editor on the Editorial Board of IEEE ACCESS,Computer Communications, and the KSII Transactions on Internet and Information Systems. MIROSLAV VOZNAK (Senior Member, IEEE) received the Ph.D. degree in telecommunications from the Faculty of Electrical Engineering and Computer Science, VSB–Technical University of Ostrava, in 2002, and the Habilitation degree, in 2009. He was appointed as a Full Professor in electronics and communications technologies, in 2017. He is the author or a coauthor of more than 100 articles in SCI/SCIE journals. His research interests include information and communication technologies, especially on the Quality of Service and Experience, network security, wireless networks, and big data analytics. He has served as a member of editorial boards for several journals, including Sensors, the Journal of Communications,Elektronika Ir Elektrotechnika, and Advances in Electrical and Electronic Engineering. 164364 VOLUME 8, 2020