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[2602.15617] DNN-Enabled Multi-User Beamforming for Throughput Maximization under Adjustable Fairness
Summary
This paper presents a DNN-based approach to optimize multi-user beamforming in wireless communications, balancing throughput and fairness through a novel optimization technique.
Why It Matters
As wireless networks grow, ensuring fairness while maximizing throughput becomes increasingly complex. This research offers a new method that leverages deep learning to address this challenge, potentially improving user experience in high-demand environments.
Key Takeaways
- Introduces a DNN-enabled method for multi-user beamforming.
- Balances throughput maximization with adjustable fairness constraints.
- Utilizes a dual-ascent algorithm for optimizing fairness and sum rate.
- Demonstrates flexibility in managing trade-offs in wireless communications.
- Contributes to the field of machine learning applications in networking.
Computer Science > Machine Learning arXiv:2602.15617 (cs) [Submitted on 17 Feb 2026] Title:DNN-Enabled Multi-User Beamforming for Throughput Maximization under Adjustable Fairness Authors:Kaifeng Lu, Markus Rupp, Stefan Schwarz View a PDF of the paper titled DNN-Enabled Multi-User Beamforming for Throughput Maximization under Adjustable Fairness, by Kaifeng Lu and 2 other authors View PDF HTML (experimental) 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. Subjects: Machine Learning (cs.LG); Networking and Internet Architecture (cs.NI) Cite as: arXiv:2602.15617 [cs.LG] (or arXiv:2602.15617v...