Improved user fairness in decode-forward relaying non-orthogonal multiple access schemes with imperfect SIC and CSI

Abstract

Non-orthogonal multiple access (NOMA) is one of the key technologies to serve in ultra-dense networks with massive connections which is crucial for Internet of Things. Besides, NOMA provides better spectral efficiency compared to orthogonal multiple access. However, NOMA systems have been mostly investigated only in terms of ergodic capacity (EC) and outage probability (OP) whereas error performances have not been well-studied. In addition, in those analysis, mostly perfect successive interference canceler (SIC) is assumed or the considered imperfect SIC model is not reasonable. Besides, channel state information (CSI) errors are also not considered in most studies. However, this is not the case for the practical scenarios, and these imperfect SIC and CSI effects limit the performance of NOMA involved systems. Moreover, the imperfect SIC causes unfairness between users. In this paper, we introduce reversed decode-forward relaying NOMA (R-DFNOMA) to improve user fairness compared to conventional DFNOMA (C-DFNOMA). In the analysis, we define imperfect SIC effect as dependant to channel fading and with this imperfect SIC and CSI errors, we derive exact expressions of EC and OP. We also provide upper bound for EC, and asymptotic and lower bound expressions for OP. Furthermore, we evaluate bit error performance of the proposed R-DFNOMA and derive exact bit error probability (BEP) in closed-form with imperfect CSI which is the first study analyzing error performances of decode-forward relaying NOMA with imperfect CSI. Then, we define user fairness index in terms of all key performance indicators (KPIs) (i.e., EC, OP and BEP). Based on extensive simulations, all derived expressions are validated, and it is proved that the proposed R-DFNOMA provides better user fairness than C-DFNOMA in terms of all KPIs. Finally, we discuss the effect of power allocations at both source and relay on the performance metrics and user fairness.