User Scheduling with Limited Feedback for Multi user Massive MIMO FDD Communications

Abstract

In modern 5G and beyond (5GandB) systems, operations such as user scheduling (US) and beamforming (BF) rely heavily on channel state information (CSI) from user equipment (UE), especially in frequency division duplexing (FDD) systems where CSI is acquired via feedback. As the number of antennas, users, and subcarriers in the system grows, the amount The efficacy of scheduling and BF depends on the available CSI and the feedback mechanisms that convey it to the base station (BS). This poses a challenge in massive multiple-input multiple-output (mMIMO) systems, particularly in multi-user (MU) FDD deployments, where large feedback overhead limits system performance. Thus, designing efficient US and BF schemes that work with reduced CSI feedback while balancing trade-offs between system capacity, feedback overhead, and fairness becomes critical. This thesis focuses on improving the sum-rate performance of MU-mMIMO FDD systems by developing user scheduling algorithms that address the trade-offs between throughput, feedback, and fairness under limited CSI feedback constraints. The aim is to design opportunistic and fairness-aware US and BF techniques using space division multiple access (SDMA), tailored for different deployment scenarios and channel conditions. To address the feedback overhead, we propose several user and antenna scheduling schemes based on limited feedback systems for both single-cell and multi-cellular environments. These schemes aim to improve multi-user diversity gain (MUD) while maintaining system performance. Specifically: A quantization and selection threshold-based US scheme is introduced to reduce feedback with minimal loss of throughput. Joint user-antenna scheduling techniques are explored to enhance scheduling efficiency with partial CSI in MU-mMIMO FDD systems. A quantized feedback-based US scheme and BF techniques for MU-mMIMO-OFDM FDD systems are discussed, optimizing performance under limited CSI.