Fast Converging Robust Beamforming for Massive MIMO in Heterogeneous Networks

• Main Authors: Malcolm M. Sande, Soumaya Hamouda, B. T. Maharaj
• Format: Article
• Language: English
• Published: IEEE 2018-01-01
• Series: IEEE Access
• Subjects: Massive MIMO; HetNet; macrocell; beamforming; matrix stuffing; ADMM algorithm
• Online Access: https://ieeexplore.ieee.org/document/8345283/

The use of massive multiple-input multiple-output (MIMO) base stations in heterogeneous networks (HetNets) offers an increase in throughput without increasing the bandwidth, but with reduced power consumption. In this paper, we investigate the optimization problem of signal-to-interference-plusnoise ratio balancing for the case of imperfect channel state information at the transmitter. We present a fast converging robust beamforming solution for macrocell users in a typical HetNet scenario with massive MIMO at the base station. The proposed method applies the matrix stuffing technique and the alternative direction method of multipliers to give an efficient solution. Simulation results of a single-cell heterogeneous network show that the proposed solution yields performance with modest accuracy, while converging in an efficient manner, compared with optimal solutions achieved by the state-of-the-art modeling languages and interior-point solvers. This is particularly for cases when the number of antennas at the base station increases to large values. This makes the solution method attractive for practical implementation in heterogeneous networks with large-scale antenna arrays at the macrocell base station.