Scheduling for Massive MIMO with Hybrid Precoding Based on Optimization and Contextual Multi-armed Bandits

Abstract

In this thesis, we study radio resource allocation (RRA) problems in fifth genera- tion (5G) systems with massive multiple-input multiple-output (MIMO) technol- ogy. We focus on optimizing the system performance (data rate maximization) of massive MIMO systems subject to quality of service (QoS) guarantees. However, these problems are extremely difficult to solve in massive MIMO, especially when practical challenges are taken into account, such as the need of a large number radio frequency (RF) chains, hybrid precoding and channel estima- tion. In order to solve the studied RRA problems in this thesis, we use as main tools optimization and contextual multi-armed bandits (CMAB). Also, this thesis is divided into two parts. The first part utilizes optimization to solve the problems of maximizing the data rate with and without considering QoS requirements. In this part, we propose a framework composed of three steps: clusterization, grouping, and scheduling. In the clusterization step, we create cluster of spatially compatible user equipments (UEs). In the grouping step, we select a set of space division multiple access (SDMA) groups from each cluster. In the scheduling step, we utilize these SDMA groups as candidates to receive resource blocks (RBs) aiming at solving a predefined RRA problem. We propose optimum and suboptimum solutions to solve the grouping and scheduling steps. The low-complexity proposed solutions present a good performance trade-off in relation to the highly complex optimal solutions and reference solutions. In the second part, we propose a framework utilizing dynamically adaptable CMAB to solve three RRA problems: i) data rate maximization; ii) data rate maximization with fairness guarantees, and; iii) data rate maximization with QoS guarantees, which are relevant problems in wireless communications. In this part, we utilize the clusterization and hybrid precoding to reduce the scheduling problem complexity by considering each cluster as an independent virtual CMAB scheduling agent. Next, we apply a new CMAB-based scheduler aiming to optimize the desired system performance metric. The solution for each problem utilizing our proposed framework is evaluated separately with UEs moving at different speeds. Simulation results showed that the proposed framework presents a good performance trade-off in data rate, fairness, and QoS in relation to the reference solutions.