I Introduction
NonOrthogonal Multiple Access (NOMA) has emerged as a spectral efficient multiple access technique for future radio access networks and has received tremendous attention from the industry and academia. It is seen as one of the most strong candidates for 5G and beyond networks due to the ability of serving massive machine type communication (MMTC) [1, 2, 3, 4] and has already taken place in 3GPPP standards [5, 6].
NOMA is firstly proposed for next generation networks in [7] and its superiority to orthogonal multiple access (OMA) counterparts is proved in terms of overall system capacity. The outage performance of NOMA is analyzed when the users are randomly deployed to emphasize the effect of path loss [8]. Then, the fairness between NOMA users is pointed out [9] and optimum power allocation (PA) algorithms are investigated for NOMA when various constraints are considered [10, 11, 12]. To show the effect of the interuserinterference (IUI) on the error performance of users, an exact biterrorrate (BER) analysis of NOMA is provided on two user singleinput singleoutput (SISO) downlink and uplink networks [13], [14]. In addition, BER union bound for multiuser downlink SISO networks is analyzed [15].
Due to its potential, the integration of NOMA with the other physical layer techniques (e.g. cooperative communication [16, 17, 18, 19, 20, 21], multipleinput and multipleoutput (MIMO) [22]) has also received great attention from researchers since its implementation into wellknown techniques is easily accomplished. The combination of cooperative communication and NOMA is considered in three manners: NOMA using in cooperative communication [16], cooperation within NOMA users [17, 18], relayaided cooperation for NOMA users [20, 21, 19]. Moreover, for the green communication concept, energy harvesting and wireless power transfer are considered for NOMA networks [23]. As more timely topics, cognitive radio in NOMA and NOMA in unnamed aerial vehicle (UAV) networks have attracted attention of researchers. In the abovementioned studies, NOMA involved systems are analyzed mostly in terms of outage probability and the ergodic sum rate and the superiority of NOMA to related OMA networks is proved.
Ia Related Works and Motivation
Spatial modulation (SM) [24] and spaceshift keying (SSK) [25] are considered as two of other promising techniques for future wireless networks. Hence, the combination of SM/SSK with NOMA has also received attention. NOMA in MIMOSM with finite alphabet inputs is proposed in [26] to enhance spectral efficiency of SM networks. Where users are grouped as only two users in a resource block and the symbols of users are transmitted by two transmit antennas simultaneously after SM is adopted for users’ data streams. The mutual information is analyzed to obtain overall spectral efficiency and the superiority of the proposed NOMAMIMOSM to MIMOOMA and MIMONOMA networks is presented. The proposed NOMAMIMOSM transmits signal with two antennas at the same time interval which needs two radio frequency (RF) chains so that the power consumption is increased. In addition, activating two transmit antennas at the same time interval increases the channel correlations. These situations are not considered in the paper. Then, in [27], authors propose a combination of SM and NOMA in a cooperative network where infrastructuretovehicle (I2V), vehicletovehicle (V2V) and intravehicle (vehicle to mobile user in car) are all included, over Rician fading channels. The network model is considered in two phases: In the first phase, NOMA is implemented for I2V and in the second phase, one of the vehicle acts as a relay for the userincar and for the other vehicle (V2V). In the second phase of communication, the relay implements NOMA after implementing SM for other vehicle’s data stream.The mutual information is analyzed and the upper bound for spectral efficiency is derived. The authors also present BER performance of proposed model via simulations, however no analytical derivations are provided for BER. The proposed models in [26, 27] attract attention to implementation of SM and NOMA together to enhance spectral efficiency of wellknown SM/NOMA networks. However, these models come with no solution to NOMA’s drawbacks such as IUI.
Despite the superiority of NOMA involved networks to OMA networks in terms of sum rate and outage performance, NOMA networks are interferencelimited. This IUI in NOMA causes a poor error performance for users compared to OMA networks [14]. Hence, the studies in the literature [1, 28] and the wireless standards [5, 6] mostly consider only two NOMA users in a resource block. The increase in users within a resource block causes dramatical growth of IUI and it limits the advantage of NOMA. In addition, it increases the number of required SIC processes in the receivers which cost high computational complexity. To overcome the decay in error performance of NOMA, SM aided multiple antenna network is proposed in [29]. The proposed model is based on applying SM principle for different users’ data streams by selecting transmit antenna according to one of user’s data and transmitting Mary modulated symbol of the other user on selected antenna. The model is considered as NOMA since the BS communicates with two users at the same time and frequency block. Ergodic sum rate simulations are provided for the proposed system. The system model in [29] is later called as spatial multiple access (SMA) and the analytical performance analysis is provided [30] to show its superiority to conventional two user NOMA networks. The SM aided NOMA network in [29] is then expanded for cooperative networks where one of the user acts as a relay for the other user and it is called as cooperative relaying system SMNOMA (CRSSMNOMA) [31]. Ergodic sum rate analysis is provided for CRSSMNOMA and it is shown that CRSSMNOMA is superior to both CRSOMA and CRSNOMA.
Although the aforementioned studies [29, 30, 31] achieve better performance than conventional NOMA systems, they still serve only two users in a resource block. To increase number of the served users in a orthogonal block (i.e., time, frequency, code), SSK combination with the NOMA is considered in [32] where the celledge user is assigned into spatial domain and two users are multiplexed with NOMA. Then, the authors in [33] have extended [32] for more than three users where celledge users are allocated to spatial domain (i.e., antenna index) and the intracell users are served by conventional NOMA and it is called as Generalized SSKNOMA (GSSSKNOMA). In [33], the complexity analysis for GSSKNOMA and BER simulations for celledge user is given. However, ML detections for celledge users provided in [32, 33] have unrealistic assumptions such as superimposed NOMA signal is perfectly known at the celledge users. Hence, the provided detectors and the analysis turn out to be wellknown conventional GSSK networks rather than GSSKNOMA networks. Since the transmitted symbol in SSKNOMA is complex, rather than only transmitting SNR such in conventional GSSK networks, a new detector for celledge user in SSKNOMA should be provided. In addition, BER simulations are presented for only celledge user, neither BER simulations for NOMA users nor the outage/capacity analysis for any users are regarded.
Furthermore, not only in SSKNOMA but also in conventional NOMA networks, the error probability analyses are very limited in the literature. To the best of authors’ knowledge, BER analysis of two user NOMA is given in [14] and the union BER bound is provided for multiuser NOMA in [15]. However, these works only consider SISO networks and there has been no work which consider multiple antenna models, yet. Since the intracell users are multiplexed by NOMA in the considered SSKNOMA network, the analysis in this paper is the first study which considers multiple antenna NOMA networks in terms of error probability.
IB Contribution
The main contributions of this paper are as follows

SSKNOMA aims to decrease IUI among NOMA users by assigning celledge user into spatial domain. Hence, all users (celledge and intracell) encounter much less IUI and have better performance (i.e., BER, sum rate, outage) than conventional NOMA networks. The IUI limits the number of users in a resource block (mostly by two users [5, 6]). SSKNOMA provides usage of one more user in a resource block under the same performance requirements of NOMA.

An optimum ML detector for celledge user in SSKNOMA is presented and the complexity analysis for proposed detector is provided. The proposed detector does not require the knowledge of transmitted superimposed signal for NOMA users as referenced in [33], hence this paper represents more realistic scenarios for wireless communications. In addition, a tight BER bound for the given detector is derived in closedform.

The closedform exact BER analysis is provided for NOMA users when the total number of users is equal to . In addition, a BER union bound is derived when the number of users is more than and arbitrary modulation constellation is considered. These analyses also stand for conventional SIMONOMA networks and this is also the first study which investigates the error performance of multiple antenna NOMA networks to the best of authors’ knowledge.

The ergodic capacity of the users and the ergodic sum rate of the overall system are analyzed and exact expressions are provided in closedform. The average outage probability of the SSKNOMA is also derived in the closedform. These analyses provide insights for analysis of uplink conventional NOMA networks.

We present extensive simulations to validate all derived theoretical analysis. Based on the simulation results, we reveal that SSKNOMA is superior to conventional NOMA in terms of all performance metrics and it allows to serve more than three users in a resource block without decreasing the performance of network which is very promising for massive type communication. In addition, limiting the required RF chain with one reduces the energy consumption at the transmitter which is very important for the energy efficiency so that for the green communication concept of 5G
IC Organization
The remainder of this paper is as follows. In Section II, the system and the channel models are introduced and the optimum detectors for the users are provided. The complexity analysis for the receivers in SSKNOMA and comparison with the conventional NOMA networks are provided. In Section III, the performance analysis of SSKNOMA is derived. Then, the evaluation of the derivations in Section III are presented via Monte Carlo simulations in Section IV. The discussion for the effect of the PA on the performance of SSKNOMA is also given in this section. Finally, the results are discussed and the paper is concluded in Section V.
ID Notation
The bold uppercase and lowercase letters denote the matrices and the vectors, respectively. We use
for transpose, for conjugate transpose and for the Frobenius form of a matrix/vector. We use for the absolute value of a scalar/vector and for the binomial coefficient.denotes the estimated symbol or index.
is the real component of a complex symbol or vector. denotes the probability of the event whereas is the probability of the event under the condition that has already occurred.Ii System and Channel Models
We consider the usage of SSKNOMA with one base station (BS) and users (i.e., ) within a resource block^{1}^{1}1 users are assumed to be assigned into a resource block. In each resource block, users with different numbers can be served and each resource block is on an orthogonal domain (frequency, time, code), hence the resource allocation algorithms for NOMA can be easily adopted in SSKNOMA in a MIMO downlink network. The BS and users are equipped with and antennas, respectively. The channel between BS and each user is represented as . consists of vectors. For user , the channel gains between each transmit antenna and each receive antenna () are assumed to be independent and identically distributed (i.i.d) as . The channels are modeled as combination of largescale fading and smallscale fading (flat fading). denotes the largescale fading coefficient determined by the distance of the th user to the BS. Without loss of generality, we assume the users are sorted in ascending order according to their distance to the BS so that the average channel gains i.e. .
As shown in Fig. 1, SSKNOMA integrates the SSK and NOMA techniques. In SSKNOMA, one of the users is assigned into spatial domain and determines transmit antenna index to be activated whereas the other users are multiplexed by NOMA. The users () are multiplexed in power domain with the different power allocation (PA) (i.e., ) coefficients. The superposition coded symbol of the users () is given by
(1) 
where is the baseband complex symbol of the th user and is PA coefficient for the th user. and . Hence, the is defined, is the total signal dimension of SC symbols and it is given as , where is the modulation level of the th user. The binary symbol of the determines the transmit antenna index (i.e., ) which transmit antenna will be activated at BS. The transmitted vector at BS contains only one nonzero element which is equal to . The transmitted vector is
(2) 
and the received vector at each user is given
(3) 
where is the transmit power and is dim additive white Gaussian noise (AWGN) at user and each dimension is distributed as . The users with receive antennas firstly implement a maximal ratio combining (MRC) and detect their own symbols. Since the celledge user and intracell users are assigned into spatial and power domain, respectively, the required detectors are implemented in different ways for those users.
Iia Detection at the first user (celledge userSM)
The detection at the first user should be implemented to obtain transmit antenna index. Although the symbols of the first user are mapped into only transmit antenna index, a SM detector must be used at the first user rather than SSK detector since the nonzero element is equal to complex SC symbol as in SM rather than only the signaltonoise ratio (SNR) value as in SSK. Nevertheless, the optimal SM detector [34] should be adopted for SSKNOMA since the transmitted symbol by active antenna is the total SC symbol of NOMA users. Considering this, the optimal detector of celledge user is given as
(4) 
where and . is defined and is the average SNR for each antenna.
IiB Detection at the users for (intracell usersNOMA)
The detections at the other users () should be implemented to obtain transmitted complex symbol of the related user. Since NOMA is implemented for the users i.e., , iterative SIC implementations should be accomplished according to decoding order. Given that the second user has the highest allocated power coefficient, the detection for is obtained by treating as noise to the other users’ symbols so that SIC is not required. Hence, the ML detection for the is given as
(5) 
where denotes the th symbol within constellation of the th user. The users () should implement iterative SIC processes and eliminate the symbols of the users which are before in the detecting order (). The ML detection is given as
(6) 
where is
(7) 
IiC Complexity
To compare the computational receiver complexity of SSKNOMA with conventional NOMA, we provide complexity analysis for the detections given in Section IIA and IIB. In the complexity analysis, we use the number of complex operations as the complexity metric. In SSKNOMA, the celledge user (i.e, ) should implement SMML detection given in (4). With the aid of [34], the complexity of the SMML detection (4) is expressed as
(8) 
The receiver complexity for intracell users (i.e, ) depends on the ML detection (5), (6) and the number of SIC process (7) which should be implemented. Hence, we firstly derive number of required ML and SIC processes for users. Since has the highest PA coefficient, implements only ML detection (5) by considering the other users’ signal as noise and no SIC process is required. On the other hand, the users (i.e., ) should implement times iterative SIC processes (7) to subtract the other users’ interference and then implements ML detection (6) to obtain own signal. Hence, the number total of ML detection to detect Mary modulated signals is given as
(9) 
and the number of required SIC processes is given as
(10) 
With the aid of [35], the complexity of ML detector (5), (6) is expressed as
(11) 
We note that the SIC processes at th user (7) includes ML detections for users . Hence, considering the ML complexity (11), the complexity of SIC processes at user is given as
(12) 
Considering the complexity of detections (8), (11), (12) and the number of the required operations (9), (10), the total receiver complexity for SSKNOMA is derived as
(13) 
To provide a fair comparison, we analyze the complexity of conventional NOMA networks when the transmit antenna is one since the SSKNOMA limits the required RF chain to only one by activating one transmit antenna according to celledge user’s data. Otherwise, the complexity of NOMA would increase exponentially by the number of transmit antenna. To obtain total complexity of NOMA, we provide the numbers of required ML and SIC processes. Since all users are multiplexed by different PA coefficients, the numbers of ML and SIC processes in NOMA are given
(14) 
and
(15) 
Recalling the complexity of ML (11) and SIC processes (12), the total receiver complexity for NOMA networks is derived as
(16) 
We present a complexity comparison of SSKNOMA and conventional NOMA for different scenarios in Table I. We assume modulation levels are same for all users in NOMA () and for fairness in terms of first user’s data rate, the number of transmit antenna in SSKNOMA is also equal to modulation level (). One can see that, SSKNOMA has less complexity than NOMA when the constellation size of users () is relatively low whereas for higher modulation levels, SSKNOMA requires more complex operations since the total size of SC symbols () becomes larger and it increases receiver complexity at the celledge user. However, considering the performance gain of SSKNOMA presented in Section IV, this complexity is considered as affordable. In addition, the other users (intracell) encounter much less receiver complexity since the number of required SIC processes becomes less.
Complexity  

3  2  2  72  108 
3  4  4  312  408 
4  2  2  140  184 
4  4  4  760  688 
5  2  2  240  280 
5  4  4  2000  1040 
Iii Performance Analysis
In this section, we provide analytical performance analyses of SSKNOMA over Rayleigh fading channels in terms of bit error probability (BEP), ergodic sum rate and outage probability. All analyses are presented in two parts as for celledge user and for intracell users since they are assigned into different domains (i.e., spatial and power) and the detections at those users (4), (5), (6) are implemented differently.
Iiia Average Bit Error Probability (ABEP) Analysis
IiiA1 ABEP for the first user ()
The exact biterror probability for the SM systems cannot be derived. Nevertheless, a tight upper bound can be obtained by using wellknown union bounding technique. Hence, the error performance of the optimal detector of in SSKNOMA given in (4) is determined by utilizing [34, Eq. (5)]
(17) 
where defines the vector which has the SC symbol of at the th index. is the Hamming distance between and . is defined as the pairwise error probability (PEP) when the is transmitted and it is estimated as . Recalling that the takes the only estimated transmit antenna index as the output of SM detection given in (4), the union bound expression (17) is simplified as
(18) 
To derive PEP expression in (18), the conditioned PEP on is given in [34, Eq. (6)] as ^{2}^{2}2However, the PEP expression is only given for real constellations (i.e., BPSK) where . For general constellations, the PEP given [34, Eq. (7)] is adopted by utilizing [36, Eq. (7)], [37, Eq. (64)] and the PEP is derived for the Rayleigh fading channels as
(19) 
where and . It is worth noting that the depends on the transmitted and estimated symbols. Since the SC is applied for the symbols, the energy level of the symbols is not constant and changes according to the PA coefficients. Hence, the PEP given in (19) should be averaged considering all scenarios. Substituting (19) into (18), the ABEP of the is derived
(20) 
IiiA2 For users
The ABEP expression is highly dependent to the modulation constellation chosen. In addition, since SC is applied at the BS for symbols of users for , iterative SIC process is required at the users for . In the presence of imperfect SIC, an error propagation from SIC process to symbols of users with higher detection order occurs and the exact error analysis becomes intractable when . Hence, we analyze exact ABEP for and QPSK is used for both users. Then an upper bound for and arbitrary constellation is derived.
ExactABEP analysis for
Proposition 1.
Considered the QPSK is chosen for both NOMA users (i.e., and ) and Gray mapping is applied, the conditional BEP of is
(21) 
Proof:
See Appendix A ∎
where denotes the baseband energy levels of the SC symbols. and are defined.
is the SNR at the output of MRC and in case Rayleigh fading, it follows Chisquare distribution with
degree of freedom and the probability density function (PDF) is given as [38, Eq. (2.321)](22) 
where . The ABEP of is obtained by . With the aid of [39, Eq. (9.6)], the ABEP of is derived as
(23) 
where
In order to obtain BEP for the we should consider two cases: whether the symbols of are detected correctly or erroneously during the SIC process at [14, Eq. (13)]. Hence, the BEP of is given as
(24) 
Proposition 2.
In the first case, we assume that symbols of are detected correctly and subtracted from the total received signal at . In this case, the BEP of is given as
(25) 
Proof:
See Appendix B ∎
where
Proposition 3.
In the second case, we assume that the symbols of are detected erroneously and subtracted from the received signal at . In this case, the BEP of is given as
(26) 
Proof:
See Appendix C ∎
where and Substituting (25) and (26) into (24), we obtain the exact BEP of the is derived as in (27) (see the top of the next page).
(27) 
By averaging (27) over instantaneous , with the aid of (22) and [39, Eq. (9.6)] we obtain that ABEP of turns out to be
(28) 
where is a constant and is defined as and .
Union bound analysis for
The error propagation from iterative SIC implementations is intractable for . Hence, we analyze union bound for BER of the users. To derive the union bound, PEP for each symbol is obtained and then averaged for all possible symbols. By utilizing PEP of SISO NOMA network [15], conditional PEP for symbols of user (i.e, ) is given
(29) 
where
(30) 
where and are defined (for proof see the [15, Eq.(17)Eq.(20)]). It is clearly seen that the SIC errors term is equal to zero for and the noise term is equal to zero for . When we averaged the conditional PEP over instantaneous SNR by using PDF (22), we derive average PEP as
(31) 
where . It is worth noting that the PEP depends on the transmitted and detected symbols of the users, hence it should be averaged all possible symbols of users. BER union bound in terms of PEP is given
(32) 
IiiB Ergodic Sum Rate Analysis
In this subsection, we analyze ergodic rate of each user by averaging achievable (Shannon) rate at each user over channel fading. Then, we obtain total ergodic capacity/sum rate of SSKNOMA to show its superiority to conventional NOMA networks.
IiiB1 Ergodic capacity of first user ()
Achievable rate of only depends on the number of the transmit antennas, since the binary symbols of the are mapped into transmit antenna index. Hence, achievable rate of is given as
(33) 
Nevertheless, the achievable rate of SSK is mostly assumed as the bits detected correctly in the literature. Hence, the achievable rate of can be given as
(34) 
where is the BEP of . To obtain the ergodic capacity of , once we calculate the , we obtain
(35) 
where is the ABEP of and given in (20).
IiiB2 Ergodic capacity for users
Achievable rate of the th (i.e., ) user is given by
(36) 
and the is defined as
(37) 
substituting (37) into (36)
(38) 
Recalling that is the SNR at the MRC output of th user, we obtain ergodic rate of the th user
(39) 
(40) 
After substituting PDF of given (22) into (39), we formulate the ergodic capacity of the user with some algebraic manipulations and the aid of [40, Eq. (4.333.5)] as in (40)(see top of the page). Where and are the exponential integral and the gamma function, respectively. and are defined.
Then the ergodic sum rate of SSKNOMA is obtained as
(41) 
IiiC Outage Probability Analysis
In this section, average outage probabilities for users are presented. Outage event is defined as the situation that the achievable rate of user cannot fulfill the quality of service (QoS) requirement of the user.
IiiC1 Outage Probability for first user ()
Outage events of the users are defined as
(42) 
where is the target rate/QoS of the th user.
For the first user, by substituting (34) into (42)
(43) 
where . The average outage probability is given
(44) 
The second integral in (44) cannot be solved in a closedform to the best of our knowledge. Nevertheless, it can be calculated by numerical tools. Furthermore, one can easily see from (44), the outage probability of the is equal to ABEP of (11) when which is expected as result of (35) and (42).
IiiC2 Outage Probability for users
For users by substituting achievable rate of users (36) into (42), we obtain the outage probability
(45) 
where . However, the outage event in (45) considers the perfect SIC for the users. The outage event for the th user also occurs when the th user (i.e., ) signals can not be detected at the th user. Hence,
Theorem 1.
We formulate the outage probability of the th user
(46) 
Proof:
See Appendix D ∎
where is defined as (47) (see the top of the next page).
(47) 
Then, we derive the average outage probability of NOMA users (i.e., ) by using the CDF of given in [38, Eq. (2.324)] and it turns out to be
(48) 
where is defined.
Iv Numerical Results
In this section we evaluate the derived expressions in Section III via Monte Carlo simulations. Unless otherwise stated, in all figures, lines denote analytical results whereas simulations are denoted by markers and the is chosen for SSKNOMA. In all simulations, we assume that and . The PA coefficients for NOMA users are fixed and chosen as , , and when the number of NOMA users is equal to, 2, 3, 4 and 5, respectively.
In Fig.2, we present the BER performances for users when and are set. QPSK is chosen for user . It is worth pointing out that exact BEP analysis match perfectly with simulations for SSKNOMA. In addition we provide conventional NOMA simulations to emphasize the superiority of SSKNOMA. We reveal that SSKNOMA outperforms NOMA significantly for all users and all users have full diversity order i.e. which can easily seen from the slope of the curves. It is worth pointing out that performance gain for celledge user becomes much more than performance gain between conventional SSK and OMA networks provided in [25], since with the increase of number of users in NOMA, IUI becomes dominant and this causes poor BER performance. Then, in order to evaluate analytical BER union bound, we provide BER performances of users i.e in Fig. 3 when and . One can easily see that the provided upper bound expressions match well with simulations.
In Fig. 4, ergodic sum rates are presented for SSKNOMA to uncover the effect of the number of users and the number of antennas. and are assumed. In addition, to emphasize superiority of SSKNOMA, ergodic capacity comparison of SSKNOMA and conventional NOMA for all users and sum rate is also provided in Fig. 5 when and . Although, conventional NOMA is proposed as a spectral efficient technique for the next wireless technologies, we reveal that SSKNOMA offers higher spectral efficiency for all users and overall system since it encounters limited IUI compared to conventional NOMA.
The outage performances of SSKNOMA are presented in Fig. 6 for users i.e. when and . The target rates of users are chosen, , and . The outage performance of is equal to BER performance of user. The analytical derivations match perfectly with simulations. In addition, to compare the outage performances of SSKNOMA and conventional NOMA, simulations are presented in Fig. 7 for two different target rates when and . In the first case, the target rates , , are chosen and SSKNOMA outperforms conventional NOMA for and . Although NOMA seems to be very few superior to SSKNOMA for in this case, in the second case, the target rates are increased to , , , and conventional NOMA remains in outage for all users until whereas the SSKNOMA has similar performance to previous case. We note that the target rate of in SSKNOMA is upgraded by just implementing more transmit antennas without any increase in complexity cost.
Finally, we investigate the effect of PA coefficients on the performance of SSKNOMA. To provide that, we present the BER and outage performance of users with the change of for at SNR in Fig. 8 and Fig. 9, respectively. The BER and outage performance of become the same when the , hence the the outage performance of is not provided in Fig. 9. The performances of users in Fig. 8 and Fig. 9 reveal that the wrong chosen of PA coefficient causes for NOMA users to communicate with low reliability (poor BER) or to be in outage. Hence, the optimal PA should be adopted according to target rate and target reliability of the system.
V Conclusion
In this paper, we provide a comprehensive analytical framework for SSKNOMA systems. We derive closedform expressions for ABEP, ergodic sum rate and outage probability of SSKNOMA. All derived expressions are validated via simulations. We reveal that, SSKNOMA outperforms conventional NOMA systems in terms of all performance metrics (i.e., BER, sum rate, outage). In this paper, we assume the fixedPA usage and then we present the effect of the PA on the BER and outage performance of SSKNOMA. The results show that the optimal PA for SSKNOMA can be obtained by considering outage and BER constraints for the chosen rate and reliability target. Since the SSKNOMA provide better performance and lower energy consumption, the combinations of SSKNOMA with the other physical techniques such as cooperative communication, energy harvesting and cognitive communication are seen as the future directions of research. The main costs in SSKNOMA can be listed in two items. Firstly, the receiver complexity at the celledge user increases exponentially according to number of users in a resource block. Secondly, the number of required transmit antennas increases with the power of 2 according to celledge user’s target rate. Recalling that most of NOMA related models limit the number of users in a resource block with only two and resource allocation/user clustering algorithms are investigated. By using these resource allocation algorithms, number of users in a resource block can be limited and then, the complexity of SSKNOMA will be much less than conventional NOMA networks with the same number of users in a resource block. Nevertheless, even for higher users in a resource block, the complexity of celledge user/firstcost is affordable considering the performance gain. Finally, the required transmit antenna number/secondcost can be reduced by implementing other index modulation techniques such as channel/mediabased modulation (MBM). Resource allocation for SSKNOMA and MBMNOMA are quite promising subjects for future researchers.
Appendix A
Considered the QPSK is used for both users with different PA coefficients, the baseband signal space of the SC symbols at BS is given Table II. In Table II, the binary symbols of the users are given in the form where the first index denotes the user and the second index represents the th bit of the th user.
Re  
Im  
Re  
Im  
Re  
Im 
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