Achievable data rate using power allocation in downlink massive MIMO systems

Abstract

The future of fifth-generation (5G) depends on the ability of massive multi-input-multi-output (MIMO) systems to support the throughput with the same bandwidth. In this paper, we derive the closed-form approximation for achievable downlink data rate expressions and comparing the performance of conventional minimum mean square error (MMSE), zero forcing (ZF) and maximum ratio transmission (MRT) precoders for a massive MIMO system when the number of antennas go to infinity. We observe that higher energy efficiency (EE) gains can be achieved by optimizing the achievable data rate dependent on using power allocation, when the number of users is equal to the number of cells. The channel prediction is able to recover part of the loss in data rate by taking into account the power consumption at the base station (BS). The performance of the data rate increases with the average signal-to-noise ratio (SNR) depending on increases in the number of antennas and the number of users. From the simulation results, the MMSE gives better performance because it is able to make the massive MIMO system less sensitive to SNR with an increasing number of antennas for the achievable data rate compared with ZF and MRT.