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

arXiv: https://arxiv.org/abs/2602.15617 · HTML: https://mlsanity.com/paper/2602.15617/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 17 references that are in the map.
- First posted on arXiv 17 Feb 2026.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [A Survey of Transformers](https://mlsanity.com/paper/2106.04554/) (2021)
- [Machine Learning for Large-Scale Optimization in 6G Wireless Networks](https://mlsanity.com/paper/2301.03377/) (2023)
- [Self-Supervised and Invariant Representations for Wireless Localization](https://mlsanity.com/paper/2302.07000/) (2023)
- [DNN-based Localization from Channel Estimates: Feature Design and Experimental Results](https://mlsanity.com/paper/2004.00363/) (2020)
- [Learning-Based Downlink Power Allocation in Cell-Free Massive MIMO Systems](https://mlsanity.com/paper/2109.03128/) (2021)
- [Transformer-Based Power Optimization for Max-Min Fairness in Cell-Free Massive MIMO](https://mlsanity.com/paper/2503.03561/) (2025)
- [Attention Aided CSI Wireless Localization](https://mlsanity.com/paper/2203.10506/) (2022)
- [Deep Learning-based mmWave MIMO Channel Estimation using sub-6 GHz Channel Information: CNN and UNet Approaches](https://mlsanity.com/paper/2506.11714/) (2025)

## Abstract (arXiv)

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.