Quan, Yuan2025-10-142025-10-142025-10-142025-10-12https://hdl.handle.net/10012/22568Beamforming and massive multi-input multi-output (MIMO) are two of the key technologies that enable high capacity and spectrum-efficient communications in 5G and beyond systems. Codebook-based HBF (Hybrid Beamforming) wherein ABF (Analog Beamforming) vectors are chosen from pre-designed codebooks and, optionally, DBF (Digital Beamforming) can be performed on the selected ABF vectors, results in lower hardware cost, training overheads, and complexity for real-time operations over FDBF (Full Digital Beamforming). We study RRM (Radio Resource Management) for the DL (Downlink) and the UL (uplink) of codebook-based HBF systems assuming proportional fairness. Our study focuses on the practical multi-channel case without assuming that the number of RF (Radio Frequency) chains at the BS (Base Station), K, is larger than the number of UE (User Equipment) in the cell, U. Indeed, in the highly practical yet underexplored case where K<U, there is a need for analog beam selection (since in most cases, not all beams with UEs can be selected together), which creates a per-time-slot constraint since the set of selected beams cannot be changed within a time slot. This constraint creates a coupling between channels in a time slot and necessitates a per-time-slot RRM solution for both DL and UL, as opposed to the per-channel approaches commonly adopted in prior work. On the DL, we address this constraint by formulating and solving a per-time-slot joint RRM optimization problem that includes beam selection, user selection, PD (Power Distribution), MCS (Modulation and Coding Scheme) selection and, optionally, DBF. This planning study helps us to understand the impact of the different parameters and options (e.g., non-equal or equal power distribution, DBF or not) and provides us with target performance to validate the online RRM algorithms that we design for equal PD and non-equal PD, respectively, with optional ZF (Zero-Forcing) DBF. The numerical results obtained for a mmWave small cell, show that our online RRM algorithms are efficient and have acceptable runtimes. On the UL, each UE has its own power budget, in contrast to the DL where the transmit power of the base station is shared among UEs. The power budget of a UE needs to be allocated per time slot to its assigned subchannels which are not known a priori. This results in the interdependence between PA (Power Allocation) and user selection in addition to the inter-channel coupling caused by beam selection, making UL RRM more challenging and inherently different compared to DL RRM. We only consider the case where ZF-DBF is enabled. We formulate a per-time-slot joint RRM optimization problem, which includes beam selection, user selection, PA, ZF-DBF, and MCS selection. This problem can only be solved for small systems. In order to obtain results for larger systems (more users and RF chains), we propose an offline heuristic that reduces the runtime necessary to obtain meaningful results while achieving performance close to the joint optimization. This offline heuristic allows us to obtain engineering insights on the impact of different system parameters as well as a target performance that we use to validate the low-complexity online heuristic that we propose (the numerical results were obtained for a mmWave small cell). Finally, we adapt and extend our solutions to a FWA (Fixed Wireless Access) setting in the mid-band and in the mmWave band where the fairness criterion is max-min so that MBR (Minimum Bit Rate) targets can be offered to all homes. We analyze the impact of codebook size and type in this setting, where the UEs are stationary home and hence beam alignment can be simplified making large codebooks feasible. Engineering insights into the system parameters and bandwidth requirements necessary to deliver MBR targets for DL and UL are provided for both bands.en5G and beyondradio resource managementanalog beamformingdigital beamformingMIMOpower distributionpower allocationdownlinkuplinkfixed wireless accessDoctoral Thesis