Efficient User Clustering, Receive Antenna Selection, and Power Allocation Algorithms for Massive MIMO-NOMA Systems

Massive multiple-input multiple-output (MIMO) and nonorthogonal multiple access (NOMA)based technologies are considered as essential parts in the 5G systems to fulfill the escalating demands of higher connectivity and data rates for emerging wireless applications. In this paper, a new approach of massive MIMO-NOMA with receive antenna selection (RAS) is considered for the uplink channel to significantly increase the number of connected devices and overall sum rate capacity with improved user-fairness and less complexity. The proposed scheme is designed from two multiuser MIMO (MU-MIMO) clusters, based on the available number of radio frequency chains (RFCs) at the base station and channel conditions, followed by power-domain NOMA for the simultaneous signal transmission. We derive the sum rate and capacity region expressions for MIMO-NOMA with RAS over Rayleigh fading channels. Then, an optimal and three highly efficient sub-optimal dynamic user clustering, RAS, and power allocation algorithms are proposed for sum rate maximization under received power constraints and minimum rate requirements of the allowed users. The effectiveness of designed algorithms is verified through extensive analysis and numerical simulations compared to the reference MU-MIMO and MIMO-NOMA systems. The achieved results show a substantial increase in connectivity, up to two-fold for the accessible number of RFCs, and overall sum rate capacity while satisfying the minimum users' rates. Besides, important tradeoffs can be realized between system performances, hardware and computational complexities, and desired user-fairness in terms of serving more users with equal/unequal rates.