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

A backhaul adaptation scheme for IAB networks using deep reinforcement learning with recursive discrete choice model

Challenges such as backhaul availability and backhaul scalability have continued to outweigh the progress of integrated access and backhaul (IAB) networks that enable multi-hop backhauling in 5G networks. These challenges, which are predominant in poor wireless channel conditions such as foliage, may lead to high energy consumption and packet losses. It is essential that the IAB topology enables efficient traffic flow by minimizing congestion and increasing robustness to backhaul failure. This article proposes a backhaul adaptation scheme that is controlled by the load on the access side of the network. The routing problem is formulated as a constrained Markov decision process and solved using a dual decomposition approach due to the existence of explicit and implicit constraints. A deep reinforcement learning (DRL) strategy that takes advantage of a recursive discrete choice model (RDCM) was proposed and implemented in a knowledge-defined networking architecture of an IAB network. The incorporation of the RDCM was shown to improve robustness to backhaul failure in IAB networks. The performance of the proposed algorithm was compared to that of conventional DRL, i.e., without RDCM, and generative model-based learning (GMBL) algorithms. The simulation results of the proposed approach reveal risk perception by introducing certain biases on alternative choices and the results showed that the proposed algorithm provides better throughput and delay performance over the two baselines.The Sentech Chair in Broadband Wireless Multimedia Communications (BWMC) and the University of Pretoria.https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639Electrical, Electronic and Computer Engineerin

Fast converging robust beamforming for massive MIMO in heterogeneous networks

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 stuf ng technique and the alternative direction method of multipliers to give an ef cient solution. Simulation results of a single-cell heterogeneous network show that the proposed solution yields performance with modest accuracy, while converging in an ef cient 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.http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639am2018Electrical, Electronic and Computer Engineerin

Access and radio resource management for IAB networks using deep reinforcement learning

Congestion in dense traf c networks is a prominent obstacle towards realizing the performance requirements of 5G new radio. Since traditional adaptive traf c signal control cannot resolve this type of congestion, realizing context in the network and adapting resource allocation based on real-time parameters is an attractive approach. This article proposes a radio resource management solution for congestion avoidance on the access side of an integrated access and backhaul (IAB) network using deep reinforcement learning (DRL). The objective of this article is to obtain an optimal policy under which the transmission throughput of all UEs is maximized under the dictates of environmental pressures such as traf c load and transmission power. Here, the resource management problem was converted into a constrained problem using Markov decision processes and dynamic power management, where a deep neural network was trained for optimal power allocation. By initializing a power control parameter, t , with zero-mean normal distribution, the DRL algorithm adopts a learning policy that aims to achieve logical allocation of resources by placing more emphasis on congestion control and user satisfaction. The performance of the proposed DRL algorithm was evaluated using two learning schemes, i.e., individual learning and nearest neighbor cooperative learning, and this was compared with the performance of a baseline algorithm. The simulation results indicate that the proposed algorithms give better overall performance when compared to the baseline algorithm. From the simulation results, there is a subtle, but critically important insight that brings into focus the fundamental connection between learning rate and the two proposed algorithms. The nearest neighbor cooperative learning algorithm is suitable for IAB networks because its throughput has a good correlation with the congestion rate.The Sentech Chair in Broadband Wireless Multimedia Communications (BWMC), University of Pretoria.http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639am2022Electrical, Electronic and Computer Engineerin