Energy-Efficiency Maximization of Hybrid Massive MIMO Precoding With Random-Resolution DACs via RF Selection

Abstract

Energy-efficiency (EE) is identified as a key 5G metric and will have a major impact on the hybrid beamforming system design. The most promising system designs include a reduced number of radio-frequency (RF) chains with digital-to-analog converters (DACs) of lower sampling resolution. However, naive reduction of beamformer components to reduce power consumption typically leads to significant loss of spectral-efficiency (SE). In this paper, we focus on the transmit beamforming (precoding) and we introduce an architecture with low-end components that maximizes the EE while minimizing the effects on SE. This is achieved by the novel design of the analog part of the precoder, where the number of the RF chains is not reduced a priori, but deactivated based on an optimization algorithm. Thus, the problem becomes a subset selection one, where only the RF chains with the optimal SE-EE performance are being activated. The selection algorithm not only determines the optimal number of RF chains to activate but also selects optimally between DACs of randomly-allocated resolution. Through simulations, we verify that the proposed architecture exhibits improved performance when compared with baseline precoding techniques which use a predefined number of RF chains with low-resolution DACs.