Joint optimization of spectral efficiency and energy efficiency with low-precision ADCs in cell-free massive MIMO systems

Abstract

In cell-free massive multiple-input multiple-output (MIMO) systems, it is beneficial to apply low-precision analog-to-digital converters (ADCs) to reduce power consumption, hardware cost, and the load on backhaul link. However, low-precision ADCs will result in serious degradation in spectral efficiency (SE). It is important to achieve a good tradeoff between SE and energy efficiency (EE) for cell-free massive MIMO systems with low-precision ADCs. In this paper, we first derive the closed-form expressions of uplink achievable rates with maximal ratio combining (MRC) receiver and zero-forcing (ZF) receiver in cell-free massive MIMO systems. Then we analyze the EE model of cell-free massive MIMO systems. Based on the above analysis, the tradeoff between SE and EE is studied. Moreover, we propose two quantization bit allocation algorithms to optimize the SE and EE jointly from the perspective of multi-objective optimization. One algorithm is based on the deep Q-network (DQN) and the other one is based on the non-dominated sorting genetic algorithm II (NSGA-II). The proposed algorithms provide more feasible solutions and achieve better system performance than equal quantization bit allocation algorithm. Numerical results verify the accuracy of the derived closed-form expressions and the effectiveness of the proposed optimization algorithms.