Non-orthogonal multiple access (NOMA) has received considerable attention as the promising technique for future wireless networks due to its superior spectral efficiency and massive connectivity [1, 2]. The pivotal feature of NOMA is that signals from the plurality of users can share and multiplex the same radio resources with different power factors based on their channel conditions. At the receiving end, the user with poor channel conditions regards other user’s messages as interference when it decodes its own message. However, the user with better channel conditions is capable of getting rid of another users’ messages by applying successive interference cancellation (SIC) before decoding its own information .
Some initial research contributions in the field of NOMA have been made by researchers [4, 5, 6, 7, 8]. More specifically, in , the authors summarized the emerging technologies from NOMA combination with multiple-input multiple-output (MIMO) to cooperative NOMA and cognitive radio (CR) NOMA, etc. In the cellular down link scenario, the outage behavior of NOMA with randomly deployed users was investigated using bounded path loss model in . In , the authors derived the outage probability under two different kinds of channel state information (CSI). The influence of user pairing with the fixed power allocation for NOMA system over Rayleigh fading channels was analyzed in . Furthermore, the performance of NOMA in large-scale underlay CR was evaluated in terms of outage probability by using stochastic-geometry . To evaluate the performance of uplink NOMA, the outage probability of more efficient NOMA schemes with power control has been derived in . The authors investigated the mutil-cell uplink NOMA transmission scenarios using Poisson cluster process , in which the rate coverage probability for the NOMA user was derived on the conditions of the different SIC schemes.
Wireless relaying technology, which has given rise to the extensive attention, is an effective way to combat the deleterious effects of fading. The outage performance of the amplify-and-forward (AF) and decode-and-forward (DF) relaying schemes were investigated in . Recently, several contributions in term of NOMA with relaying have been researched [12, 13, 14, 15, 16], which can improve the spectrum efficiency and transmit reliability of wireless network. Cooperative NOMA scheme was first proposed in which users with better channel conditions are delegated as relaying nodes . As such, the communication reliability for the users far away from the base station are enhanced. The coordinated two-point system with superposition coding (SC) was investigated in the down link communication . In order to improve energy efficiency, the authors have considered the simultaneous wireless information and power transfer (SWIPT) to NOMA , in which stochastic geometry has been utilized to model the positions of users and the near user is regarded as a energy harvesting DF relay to help far user. The outage probability and achievable average rate of NOMA with DF relaying were analyzed over Rayleigh fading channels . The author in  proposed relay-aided multiple access (RASA), in which the near user exploiting the two way relaying protocol to help far user. Additionally, for solving the potential time slot wasted brought by half-duplex relaying protocol, the outage performance of full-duplex device-to-device based cooperative NOMA was researched in .
I-a Motivation and Contributions
While the aforementioned literature laid a solid foundation for the role of NOMA in Rayleigh fading, the impact of cooperative NOMA in Nakagami- fading has not been well understood. Based on the different parameter settings, Nakagami- fading channel can be reduce to multiple types of channel. For instance, the Gaussian channel and Rayleigh fading channel are the special cases of its. In , the authors investigated the spectrum-sharing CR network based cooperative NOMA over Nakagami- fading channels. The outage performance of NOMA with channel sorted referring the variable gain AF relaying have been researched in , but the impact of NOMA in terms of the direct link transmission in Nakagami- fading was not considered. Therefore, the prior work in  motivates us to develop this research contribution.
To the best of our knowledge, the performance of NOMA with sorted channel referring to the BS with fixed gain AF relaying over Nakagami- fading channels is not researched yet. Additionally, as stated in , the author did not investigate the outage performance of downlink NOMA system over Nakagami- fading channels. Motivated by these, we address two NOMA transmission scenarios in this paper: 1) The first scenario is that the BS intends to communicate with multiple users through the assistance of AF relaying, where the direct links are existent between the BS and users; and 2) AF relaying is not existent between the BS and users. The primary contributions of this paper are summarised as follows:
We first derive the closed-form expressions of outage probability for the sorted NOMA users. To obtain more insights, we further derive the asymptotic outage probability of the users and obtain the corresponding diversity orders. We demonstrate that NOMA is capable of outperforming OMA in terms of outage probability over Nakagami- fading channels. We observe that when several users’ quality of service (QoS) are met at the same time, NOMA can offer better fairness.
Additionally, we analyze the delay-limited transmission throughput for both scenarios based on the analytical results. It is worth noting that NOMA can achieve larger throughput with regard to conventional MA in more general channels.
The rest of this paper is organized as follows. Section II describes the system model for studying NOMA with the fixed gain AF relaying over Nakagami- fading channels. In Section III, the exact and asymptomatic expressions of outage probability for the users are derived in two scenarios. Numerical results are presented in Section IV for verifying our analysis, and are followed by our conclusion in Section V.
Ii System Model
This paper considers two insightful scenarios which are the downlink single cell cooperative communication scenario with a fixed gain AF relaying and non-cooperative communication scenario, respectively. For the sake of simplicity, the BS, a AF relaying node and two paired users which include near user and far user are presented as shown in Fig. 1, where the relaying node can be existent or inexistent. All the nodes are equipped with single antenna. The complex channel gain between the BS and users, between the BS and AF relaying node, and between the AF relaying node and users are denoted as , and , respectively. Without loss of generality, the channel gains of users are sorted as 111In this paper, we only focus our attention on investigating a sorted pair of users in which user 1 and user 2 can be selected or user 1 and user 3 are selected for performing NOMA jointly in the first scenario.
. All the complex channel gains are modeled as independent and identically distribution (i.i.d) random variables RVswhich is subject to Nakagami- distribution .
The transmission powers for the BS and the AF relaying node are assumed to be equal, i.e.,
. The energy of the transmitted signal is normalized to one. Meanwhile, the additive white Gaussian noise (AWGN) terms of all the links have zero mean and variance.
Ii-a The First Scenario
For the first scenario, the whole communication processes are completed in two slots. During the first slot, the BS transmits superposed signal to the relaying node, and according to the NOMA scheme . and are the power allocation coefficients for and , where , . and are the signal for and , respectively. By stipulating this assumption, SIC can be invoked by for first detecting having a larger transmit power, which has less inference signal. Accordingly, the signal of is detected from original superposed signal. The observation at the relaying node, and are given by
where , and are AWGN at the relaying node, and , respectively. The received signal to interference and noise ratio (SINR) for to detect is given by
where is transmit signal to noise ratio (SNR). SIC is first performed for by detecting and decoding the ’ information. Then, the received SINR at is given by
After the far user message is decoded, can decode its own information with the following SINR
During the second slot, the relaying node amplifies the received signal and forwards to and using the fixed gain factor , where denotes expectation operation. The signals received at and is expressed as
respectively, where and denote the AWGN at and , respectively. The received SINR for to detect is given by
where . first detect ’s information with the received SINR given by
and then after SIC operations, the receiving SINR for is given by
Ii-B The Second Scenario
On the basis of the above scenario, another scenario considered in this paper is that the AF relaying node is assumed to be absent with randomly user deployment.
For the second scenario, the BS transmits the superposed signals to all the users based on the NOMA scheme. Therefore, the signal received at the -th user is written as
where denotes the Nakagami- fading channel gain from the BS to the -th user. is the power allocation coefficient for the -th user with , while it satisfies the relationship for . denotes the signal for the -th user and is AWGN at the -th user. Thus, SIC is employed at the -th user and the receiving SINR for the -th user to detect the -th user is given by
After users can be detected successfully, the received SINR for the -th user is given by
Iii Performance evaluation
In this section, the performance of two scenarios are characterized in terms of outage probability as follows.
Iii-a Outage Probability
It is significant to examine the outage probability when the user QoS requirements can be satisfied in the communication system just as in . The outage probability of the users over Nakagami- fading channels is analyzed for two different scenarios.
From the above explanations, the probability density function (PDF) foris expressed as
where is the Gamma function, and denote the parameters of the multipath fading and the control spread, respectively. Therefore,is expressed as 
respectively, where is the average power.
With the aid of order statistics  and binomial theorem, the PDF and CDF of the th user s channel gain can be expressed as
respectively, where is the unsorted channel gain between the BS and an arbitrary user.
Iii-A1 Outage Probability for the First Scenario
In this scenario, the users combine with the observations from the BS and the relaying node by using selection combining at the last slot. Therefore, an outage event for can be interpreted as two reasons, i.e., it cannot detect its own message at both slots. Based on the above explanation, the outage probability of is given by
where with being the target rate at .
The following theorem provides the outage probability of in the this scenario.
The closed-form expression for the outage probability of the investigated is expressed as
where with . denotes the number of users in the considered scenario, , , , . denotes the -th user (far user). and denote the average power for the links between the BS and the relaying node, and between the relaying node and , respectively. is the modified Bessel function of the second kind with order .
See Appendix A. ∎
According to NOMA scheme, the outage would not occur for in two situations where can detect ’s information and also can detect its own information during the two slots. Furthermore, the outage probability of is given by
where with being the target rate at .
The following theorem provides the outage probability of in this scenario.
The closed-form expression for the outage probability of the investigated is expressed as
where , , , , , denotes the average power of the link between the relaying and . denotes the -th user (near user).
See Appendix B. ∎
Iii-A2 Outage Probability for the Second Scenario
In this scenario, the SIC is carried out at the -th user by detecting and canceling the -th user s information before it detects and decodes its own signals in terms of NOMA protocol. If the -th user cannot detect the discretionary -th user s information, outage occurs. Therefore, after some manipulations such as in , the outage probability of -th user can be expressed as follows:
where , for , , with being the target rate at th user. Note that (24) is obtained under the condition of .
Iii-B Diversity Analysis
In this section, to gain more insights, the diversity order achieved by the users for two scenarios can be obtained based on the above analytical results. The diversity order is defined as
When , the approximate expressions of CDF for the unsorted channel gain and the -th user’s sorted channel gain are given by 
where represents “be proportional to”.
can be rewritten as follows:
Similar to (III-B), the asymptotic outage probability for can be expression as
Remark 1 and Remark 2 provide insightful guidelines for exploiting the direct link between the BS and users over more general fading channels. The diversity order of the user is relevant to the parameter .
Iii-C Throughput Analysis
In this section, the delay-limited transmission mode is considered for two scenarios over Nakagami- fading channels. The BS sends information at a constant rate and the system throughput is subjective to the effect of outage probability. It is important to investigate the system throughput in the delay-limited mode for practical implementations. Therefore, the system throughput in the first scenario is expressed as
Additionally, based on the analytical results for the outage probability in the second scenario, the system throughput with the constant rates is expressed as
where can be obtained from (24).
Iv Numerical Results
In this section, the numerical results are provided to verify the validity of the derived theoretical expressions for two scenarios over Nakagami- fading channels. Without loss of the generality, the conventional orthogonal multiple access (OMA) is intended as the benchmark for comparison, where the better user is scheduled. The target rate for the orthogonal user is equal to bit per channel user (BPCU).
Iv-a The first scenario
In the first scenario, the distance between the BS and users is normalised to unity. Let denotes the distance between the BS and fixed gain relaying. The average power and can be attained, where is pathloss exponent setting to be . The power allocation coefficients are , for . The target rate for the near user and far user are assumed to be and BPCU, respectively. The fixed gain for AF relaying is assumed to be .
Fig. 2 plots the outage probability of two users versus SNR with . The exact outage probability curves of two users for NOMA over Nakagami- fading channels are given by numerical simulation and perfectly match with the theoretical results derived in (1) and (2), respectively. The asymptotic outage probability curves of two users are plotted according to (III-B) and (III-B), respectively. Obviously, the asymptotic curves well approximate the exact curves in the high SNR. We can observe that NOMA is capable of outperforming OMA in terms of outage probability. Additionally, Fig. 3 plots the theoretical results of outage probability versus SNR with and . It is observed that the considered cooperative NOMA system has lower outage probability with the parameter increasing. This phenomenon can be explained is that the high SNR slope for outage probability is becoming more larger.
Fig. 4 plots the system throughput versus SNR in delay-limited transmission mode for the first scenario. The solid curves represent throughput with different values of which is obtained from (35). The dashed curves represent throughput of conventional OMA. As can be observed from the figure, the higher system throughput can be achieved with increasing the values of at the high SNR. This phenomenon can be explained as that this scenario has the lower outage probability on the condition of the larger values of . It is worth noting that NOMA achieve larger system throughput compared to conventional OMA.
Iv-B The second scenario
In the second scenario, we assume that there are three users considered setting to be . The average powers between the BS and three users are , and , respectively. The power allocation coefficients are , and . The target rate for each user is assumed to be , , BPCU, respectively. Similarly, the fixed gain for the AF relaying is also assumed to be .
Fig. 5 plots the outage probability of three users versus SNR with . The solid curves represent the outage probability of three users for NOMA which are obtained from (III-A2). Obviously, the exact outage probability curves match precisely with the Monte Carlo simulation results. The dashed curves represents the asymptotic outage probability which is obtained from (34). The asymptotic curves well approximate the exact performance curves in the high SNR. It is shown that NOMA is also capable of outperforming orthogonal multiple access (OMA) in terms of outage probability in this scenario. Another observation is that when several users’ QoS are met at same time, NOMA scheme offers better fairness with regard to conventional OMA. It is worth pointing out that NOMA and OMA has the same outage probability slope for user 3, which means that they achieves the same diversity. However, the different diversity orders are obtained for user 1 and 2, respectively. Fig. 6 plots the theoretical results of outage probability versus SNR with and . It is worth noting that NOMA system can achieve lower outage performance with the parameter increasing. The reason is that a larger results in higher diversity order for each user, which in turn leads lower outage probability.
Fig. 7 plots the system throughput versus SNR in delay-limited transmission mode for the second scenario. The solid curves represent throughput which is obtained from (36) with different values of . Similarly, one can observe that the higher system throughput can be achieved with the values increasing at the high SNR. As can be seen from the figure, the throughout ceiling exits in the high SNR region. This is due to the fact that the outage probability is tending to zero and throughput is determined only by the target rate.
From the above analysis results, we observe that the second scenario can be regarded as a benchmark of cooperative NOMA scenario considered in this paper. For the purposes of comparison, two pairing users (user 1 and user 3) are selected to perform NOMA jointly. The power allocation coefficients for user 1 and user 3 are and . The target rate for user 1 and user 3 are set to be , BPCU, respectively. Fig. 8 plots the outage probability of two scenarios versus SNR with . One can observe that the outage performance of cooperative NOMA scenario is superior to the second scenario. This is due to the fact that cooperative NOMA system can provide larger diversity order relative to the second scenario.
In this paper the outage performance of NOMA with the fixed gain AF relaying over Nakagami- fading channels has been investigated. First, the outage behavior of the ordered users by using the AF relaying protocol was researched in detail when the direct links between the BS and the users exist. Second, new closed-form expression for the outage probability with stochastically deployed users was provided under the condition of no relaying node. Based on the analytical results, the diversity orders achieved by the users for the two scenarios have been obtained. Furthermore, it is observed that the fairness of multiple users can be ensured by using NOMA scheme in contrast to conventional MA. Additionally, these derived results clarified the outage performance of NOMA scheme with cooperative technology over more general fading channels. Finally, the performance of these two scenarios were compared in terms of outage probability. Assuming the direct links were existent in the first scenario, hence our future research may consider comparing the performance between having direct links and no direct links.
Appendix A: Proof of Theorem 1
and are calculated as follows:
where is established on the condition of .
where (Appendix A: Proof of Theorem 1) follows Binomial theorem and (A.4) is obtained by using [Eq. (3.471.9)] in . Substituting (Appendix A: Proof of Theorem 1) and (A.4) into (Appendix A: Proof of Theorem 1), we can obtain (1).
The proof is completed.