Massive MIMO-NOMA Networks With Imperfect SIC: Design and Fairness Enhancement

This paper addresses multi-user multi-cluster massive multiple-input-multiple-output (MIMO) systems with non-orthogonal multiple access (NOMA). Assuming the downlink mode, and taking into consideration the impact of imperfect successive interference cancellation (SIC), an in-depth analytical analysis is carried out, in which closed-form expressions for the outage probability and ergodic rates are derived. Subsequently, the power allocation coefficients of users within each sub-group are optimized to maximize fairness. The considered power optimization is simplified to a convex problem, which makes it possible to obtain the optimal solution via Karush-Kuhn-Tucker (KKT) conditions. Based on the achieved solution, we propose an iterative algorithm to provide fairness also among different sub-groups. Simulation results alongside with insightful discussions are provided to investigate the impact of imperfect SIC and demonstrate the fairness superiority of the proposed dynamic power allocation policies. For example, our results show that if the residual error propagation levels are high, A. S. de Sena and P. H. J. Nardelli are with the Lappeenranta University of Technology, Finland (email: arthurssena@ieee.org, pedro.nardelli@lut.fi). F. R. M. Lima and D. B. da Costa are with the Federal University of Ceará, Brazil (email: rafaelm@gtel.ufc.br, danielbcosta@ieee.org). Z. Ding is with the University of Manchester, UK (email: zhiguo.ding@manchester.ac.uk). U. S. Dias is with the University of Brası́lia, Brazil (email: ugodias@ieee.org). C. B. Papadias is with the Athens Information Technology, Greece (email: papadias@ait.edu.gr). Part of this paper has been submitted in IEEE International Conference on Communications (ICC 2020), Dublin, Ireland. © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.