DNN-Enabled Multi-User Beamforming for Throughput Maximization under Adjustable Fairness

TL;DR

This paper introduces a deep learning-based method using wireless transformers to optimize multi-user beamforming, balancing fairness and throughput in wireless networks through a controllable, adaptive approach.

Contribution

It presents a novel unsupervised learning framework that combines sum rate and fairness objectives with automatic trade-off control via dual ascent, improving multi-user beamforming optimization.

Findings

Flexible fairness-throughput trade-off management

Effective Pareto front approximation in beamforming

Enhanced control over fairness constraints

Abstract

Ensuring user fairness in wireless communications is a fundamental challenge, as balancing the trade-off between fairness and sum rate leads to a non-convex, multi-objective optimization whose complexity grows with network scale. To alleviate this conflict, we propose an optimization-based unsupervised learning approach based on the wireless transformer (WiT) architecture that learns from channel state information (CSI) features. We reformulate the trade-off by combining the sum rate and fairness objectives through a Lagrangian multiplier, which is updated automatically via a dual-ascent algorithm. This mechanism allows for a controllable fairness constraint while simultaneously maximizing the sum rate, effectively realizing a trace on the Pareto front between two conflicting objectives. Our findings show that the proposed approach offers a flexible solution for managing the trade-off optimization under prescribed fairness.