Resource Allocation in 6G with Massive MIMO via Multi-Objective Optimization Approach for Throughput and Energy Efficiency

Abstract

Most of the works in this area rely on characterizations based on ideal conditions, which lead to unrealistic conclusions [3]-[5].This paper studies resource allocation problems in multi-cell 6G massive MIMO networks and addresses throughput, energy efficiency and user fairness objectives under practical feasible sets. Hence, we formulate a multi-objective optimization problem over hybrid beamforming, OFDMA subcarrier allocation and power allocation. The proposed framework considers several practical 6G network conditions, including imperfect channel state information, pilot contamination, user mobility, inter-cell interference, and a detailed power-consumption model. In the simulation study, three optimization solvers are evaluated: the interior-point method, sequential quadratic programming, and the active-set method. The results show that the interior-point method provides the best overall performance in terms of throughput and fairness, achieving 1.947 × 10⁹ bps and a fairness index of 0.9815. In contrast, the active-set method achieves the highest energy efficiency, ranging between 1.41 × 10⁷ and 1.43 × 10⁷ bits/J. Overall, the main contribution of this study is the development of a practical 6G resource allocation framework that balances throughput, energy efficiency, and fairness under realistic network conditions.