Energy and spectral efficiency tradeoff in massive MIMO systems with multi-objective adaptive genetic algorithm

Spectral efficiency (SE) and energy efficiency (EE) are both important metrics in massive multiple-input multiple-output (MIMO) systems. However, maximizing EE and SE is always conflicting with each other, and they can hardly be achieved simultaneously. In this paper, we focus on the tradeoff optimization between EE and SE in multiuser massive MIMO systems in terms of the number of transmit antennas and the transmit power. Different from the previous EE-oriented or SE-oriented method, the EE-SE tradeoff problem is formulated into a multi-objective optimization problem. To efficiently attain the Pareto optimal front (POF) of EE-SE tradeoff, a multi-objective adaptive genetic algorithm, inspired by the non-dominated sorting genetic algorithm (NSGA-II), is proposed to improve the convergence speed. Experimental comparisons against several well-known multi-objective algorithms show that the proposed algorithm can quickly adapt to the true POF of EE-SE tradeoff and maintain good performance on benchmark functions in terms of the adopted performance metrics.