This paper studies the radio resource management (RRM) for the uplink (UL) of a cellular system with codebookbased hybrid beamforming. We consider the often neglected but highly practical multi-channel case with fewer radio frequency chains in the base station than user equipment (UEs) in the cell, assuming one RF chain per UE. As for any UL RRM, a per-time slot solution is needed as the allocation of power to subchannels by a UE can only be done once it knows which subchannels it has been allocated. The RRM in this system comprises beam selection, user selection and power allocation, three steps that are intricately coupled and we will show that the order in which they are performed does impact performance and so does the amount of coupling that we take into account. Specifically, we propose 4 online sequential solutions with different orders in which the steps are called and of different complexities, i.e., different levels of coupling between the steps. Our extensive numerical campaign for a mmWave system shows how a well-designed heuristic that takes some level of couplings between the steps can make the performance exceedingly better than a benchmark.

We study the uplink resource management of a multi-user multiple-input-multiple-output single cell for Zero-Forcing receive combining transmission. We consider jointly power allocation, user selection and modulation and coding scheme selection over multiple subchannels. Our contributions are twofold: we first propose a quasi-optimal offline algorithm that provides a target performance and then design and validate an efficient online proportional fair algorithm that performs the above steps. Due to user power constraints, the offline optimization is conducted jointly for all subchannels within a time slot, a computationally intensive task, prompting the proposal of a greedy offline algorithm that we validate in two ways: 1) for a small number of users, by solving the general problem to quasi-optimality and 2) for a larger number of users, by solving again to quasi-optimality a transformed version of the general problem when the channels are assumed flat. From the offline study, we find that, given the right user selection, equal power allocation can be employed without much degradation in performance. We also see that the number of channels allocated to users varies widely depending upon their channel gains. Using these insights, we propose our efficient real-time online algorithm that has runtime competitiveness with a state-of-the-art benchmark.