Power Minimization for Massive MIMO Systems with Two-Timescale Hybrid Precoding

Abstract

Recently, a two-timescale hybrid precoding (THP) scheme has been proposed to reduce the implementation cost of massive MIMO systems. In THP, the MIMO precoder consists of a high- dimensional RF precoder adaptive to the channel statistics and a low-dimensional baseband precoder adaptive to the instantaneous effective channel state information (CSI). Since the channel statistics changes at a slow timescale and is approximately the same for different subbands, only a single RF precoder is required to cover all subbands over a long term, which helps to reduce the hardware cost and implementation complexity of RF precoder. Moreover, the CSI signaling overhead can also be reduced. In this paper, we consider power minimization for massive MIMO systems with THP and individual average rate constraints. Due to the two-timescale design and individual average rate constraints, the problem is a challenging non-convex stochastic optimization problem. We propose an online constrained stochastic successive convex approximation (CSSCA) algorithm to find a stationary point of the power minimization problem. Simulations show that the proposed algorithm achieves significant gains over various baseline algorithms.