Fast convergence for efficient beamforming in massive MIMO heterogeneous networks

Abstract

Massive multiple-input multiple-output (MIMO) is an emerging technology, which is an enabler for future broadband wireless networks that support connection of the internet of people and internet of things. The use of massive MIMO base stations and heterogeneous networks offers an increase in throughput without increasing the bandwidth, but with reduced power consumption. This makes the two technologies very attractive for future wireless mobile communication systems. We investigate the optimization problem of maximizing the data rate whilst mitigating inter-tier interference for macrocell users in a typical heterogeneous network. We present a first-order solution for maximizing the weighted sum rate of the macrocell users. The proposed method uses the matrix stuffing technique and the alternative direction method of multipliers to efficiently transform and solve the optimization problem, respectively. Simulation results of a single-cell heterogeneous network show that the solution gives near-optimal performance, which is achieved by state-of-the-art modelling languages and second-order solvers for an increased number of antennas at the macrocell base station. The proposed method converges much faster than the interior-point methods, which makes it attractive for practical implementation.