On the Energy Efficiency of Multi-Cell Massive MIMO With Beamforming Training

This paper is concerned with a multi-cell downlink (DL) massive multiple-input multiple-output (MIMO) system operating over spatially correlated Rician fading channels. We not only consider estimating channel state information (CSI) at the base station (BS), but also adopt beamforming training (BT) to obtain CSI at the users. With maximum-ratio transmission (MRT) or zero-forcing (ZF) employed at the BS to process the transmit signals, the paper derives closed-form expressions of the sum spectral efficiency for both cases: with and without BT. Based on the obtained closed-form expressions, we investigate the effect of the DL pilot length on the system performance for MRT and ZF precoding and with or without BT. Moreover, when the DL pilot length falls within different ranges, we find out whether using BT leads to better system performance. To address the energy efficiency (EE) maximization problem under the constraints of a given sum spectral efficiency and a maximum total DL transmit power, we transform the problem into a geometric program (GP), which can be solved more efficiently. In particular, we develop one iterative power allocation algorithm for the system with BT scheme. Simulation and numerical results demonstrate that the proposed power allocation algorithm can improve the system EE. Numerical results also show that when the BS uses MRT precoding, the sum spectral efficiency in the high signal-to-noise ratio (SNR) region is much improved with BT.