Massive MIMO-NOMA Networks with Imperfect SIC: Design and Fairness Enhancement
preprintposted on 2020-01-21, 23:52 authored by Arthur Sousa de SenaArthur Sousa de Sena, Pedro NardelliPedro Nardelli
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, the employment of orthogonal multiple access (OMA) is always preferable than NOMA. It is also shown that the proposed power allocation outperforms conventional massive MIMO-NOMA setups operating with fixed power allocation strategies in terms of outage probability.
This project is partly supported by Academy of Finland via: (a) ee-IoT project under Grant n.319009, (b) FIREMAN consortium under Grant CHIST-ERA/n.326270, and (c) EnergyNet research fellowship under Grants n.321265 and n.328869.
Email Address of Submitting Authorarthurssena@ieee.org
ORCID of Submitting Authorhttps://orcid.org/0000-0002-3719-2697
Submitting Author's InstitutionLappeenranta University of Technology
Submitting Author's Country
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in IEEE Transactions on Wireless Communications