Two-Side Coalitional Matching Approach for Joint MIMO-NOMA Clustering and BS Selection in Multi-Cell MIMO-NOMA Systems

Abstract

Resource management in multi-cell multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) is challenged by computational complexity, flexible clustering, and potential channel correlation. In this paper, we focus on a combined resource allocation problem: NOMA mobile user (MU) clustering and the base station (BS) selection, to improve system data rate. Different from sum data rate maximization and max-min fairness, we introduce a new objective function, i.e., relative fairness, which integrates MU fairness into system data rate optimization to overcome the domination effect of BS in advantaged situations of sum data rate improving. Moreover, we derive the closed form solution of MIMO-NOMA resource allocation for a single cluster, and it can be employed for any size of cluster. Furthermore, we propose a new two-side coalitional matching approach to jointly optimize MIMO-NOMA clustering and BS selection, which is able to balance the tradeoff between MUs’ individual benefits and the overall network performance. The proposed approach is core stable. Pauta-criterion is employed on system performance evaluation to provide a judgement on win-win solutions. In simulation, extensive comparisons provide insightful understanding of our proposed MIMO-NOMA clustering strategy, relative fairness, and the proposed two-side coalitional matching approach.