Energy-Efficient Hybrid Precoding for mmWave Massive MIMO Systems

Abstract

In millimeter Wave (mmWave) massive multi-input multi-output (MIMO) systems, the novel sub-connected architecture has been introduced which can further reduce power consumption compared with the fully-connected architecture. In this paper, we propose an energy-efficient hybrid precoding design with quality of service (QoS) constraints for sub-connected architecture. Firstly, the total energy efficiency optimization problem with nonconvex constraints is decomposed into two separate optimization sub-problems whose solution is a optimal single precoder respectively. Then, the first optimization sub-problem analog domain concerned is solved by a cross-entropy-based algorithm according to the machine learning theory. Finally, the second optimization sub-problem digital domain concerned is solved by an iterative optimization algorithm from fractional programming theory. Simulation results demonstrate that the performance of the proposed precoding algorithm. The performance of the proposed algorithm is capable of achieving near optimal solution. It is also demonstrated that the proposed algorithm can enhance the energy efficiency of networks while guaranteeing the QoS of users.