Scalable Coordinated Beamforming for Dense Wireless Cooperative Networks

To meet the ever growing demand for both high throughput and uniform coverage in future wireless networks, dense network deployment will be ubiquitous, for which co- operation among the access points is critical. Considering the computational complexity of designing coordinated beamformers for dense networks, low-complexity and suboptimal precoding strategies are often adopted. However, it is not clear how much performance loss will be caused. To enable optimal coordinated beamforming, in this paper, we propose a framework to design a scalable beamforming algorithm based on the alternative direction method of multipliers (ADMM) method. Specifically, we first propose to apply the matrix stuffing technique to transform the original optimization problem to an equivalent ADMM-compliant problem, which is much more efficient than the widely-used modeling framework CVX. We will then propose to use the ADMM algorithm, a.k.a. the operator splitting method, to solve the transformed ADMM-compliant problem efficiently. In particular, the subproblems of the ADMM algorithm at each iteration can be solved with closed-forms and in parallel. Simulation results show that the proposed techniques can result in significant computational efficiency compared to the state- of-the-art interior-point solvers. Furthermore, the simulation results demonstrate that the optimal coordinated beamforming can significantly improve the system performance compared to sub-optimal zero forcing beamforming

Fast converging robust beamforming for downlink massive MIMO systems in heterogenous networks

Massive multiple-input multiple-output (MIMO) is an emerging technology, which is an enabler for future broadband wireless networks that support high speed connection of densely populated areas. Application of massive MIMO at the macrocell base stations in heterogeneous networks (HetNets) offers an increase in throughput without increasing the bandwidth, but with reduced power consumption. This research investigated the optimisation problem of signal-to-interference-plus-noise ratio (SINR) balancing for macrocell users in a typical HetNet scenario with massive MIMO at the base station. The aim was to present an efficient beamforming solution that would enhance inter-tier interference mitigation in heterogeneous networks. The system model considered the case of perfect channel state information (CSI) acquisition at the transmitter, as well as the case of imperfect CSI at the transmitter. A fast converging beamforming solution, which is applicable to both channel models, is presented. The proposed beamforming solution method applies the matrix stuffing technique and the alternative direction method of multipliers, in a two-stage fashion, to give a modestly accurate and efficient solution. In the first stage, the original optimisation problem is transformed into standard second-order conic program (SOCP) form using the Smith form reformulation and applying the matrix stuffing technique for fast transformation. The second stage uses the alternative direction method of multipliers to solve the SOCP-based optimisation problem. Simulations to evaluate the SINR performance of the proposed solution method were carried out with supporting software-based simulations using relevant MATLAB toolboxes. The simulation results of a typical single cell in a HetNet show that the proposed solution gives performance with modest accuracy, while converging in an efficient manner, compared to optimal solutions achieved by state-of-the-art modelling languages and interior-point solvers. This is particularly for cases when the number of antennas at the base station increases to large values, for both models of perfect CSI and imperfect CSI. This makes the solution method attractive for practical implementation in heterogeneous networks with large scale antenna arrays at the macrocell base station.Dissertation (MEng)--University of Pretoria, 2018.Electrical, Electronic and Computer EngineeringMEngUnrestricte