Flexible Resource Allocation in IRS-assisted Systems using Hypernetworks

Abstract

Flexible resource allocation in intelligent reflecting surface (IRS)-assisted systems is necessary to account for fairness as well as time-varying channel behavior and user service priorities. An IRS-assisted system can achieve this flexibility by assigning different weights to each user when optimizing resources and the IRS configuration. In this paper, for the first time, we propose a hypernetwork-based beamforming (HNB) framework to dynamically leverage pilot information and user weights to generate the beamforming vectors and IRS configuration that maximize the weighted sum-rate (WSR) in a multi-user IRS-assisted system. As opposed to a traditional learning approach where a beamforming network (BFN) is trained once to optimize the WSR for every possible set of user weights, in a hypernetwork approach, a hypernetwork is trained to generate the learning parameters of the BFN conditioned on the input user weights, i.e., the BFN parameters are now adapted to the user weights without the need for any retraining. Numerical experiments corroborate the effectiveness of the proposed HNB framework to provide performance close to (within approximately 15−17% of) the optimistic benchmark produced by a numerical block-coordinate descent (BCD) algorithm that assumes perfect channel state information (CSI) knowledge. Moreover, the HNB trained with only a few epochs outperforms traditional fully-trained deep learning methods such as fully connected neural networks (FCNN) and graph neural networks (GNN). For example, in one considered scenario, the HNB nearly halves the gap to the BCD-with-CSI performance to 16% compared to gaps of 31% and 28% associated with FCNN and GNN schemes, respectively.