Load-Balancing During Resource Allocation in Multi-Cell MIMO-NOMA

Load balancing during resource allocation (RA) in multicell multi-input multi-output non-orthogonal multiple access (MC-MIMO-NOMA) poses significant challenges for the deployment of massive MIMO-NOMA in next-generation networks. This paper introduces load-balancing algorithms to address RA challenges in massive MIMO-NOMA systems. First, we formulate the general RA problem as a load minimization problem, involving the joint optimization of resource allocation and user clustering. To minimize computation complexity, this problem is then reduced to a suboptimal RA problem within each cell. However, due to the non-convex nature of the problem, obtaining an optimal solution is not straightforward. To overcome this, we propose the power to targeted load optimization (PTLO) approach to solve the RA problem in each cell based on underload and overload scenarios. By leveraging the PTLO approach, we develop a user clustering strategy using branch-and-bound and weighted matching theory to optimize RA in each cell. Through simulations, we evaluate the performance of the proposed load-balancing algorithms (LB-BB-MIMO-NOMA and LB-M2O-MIMO-NOMA) against benchmark algorithms such as Correlated-MIMO-NOMA, Coalition-MIMO-NOMA, Gain-Diff-MIMO-NOMA, Greedy-MIMO-NOMA, and MIMO-OMA. The results demonstrate that the proposed algorithms consistently outperform the benchmarks. Furthermore, we provide a detailed analysis highlighting the efficiency of the proposed algorithms in the MC-MIMO-NOMA networks.