用AI动态调节公平性，提升多用户通信吞吐量
DNN-Enabled Multi-User Beamforming for Throughput Maximization under Adjustable Fairness
- 基于无线Transformer架构，从信道信息中学习波束成形策略
- 通过拉格朗日乘子自动调节公平性，实现吞吐量与公平性的帕累托最优
- 适合需要灵活平衡速率与公平性的大规模无线系统设计
在无线通信中保障用户公平性是一项基本挑战，因为公平性与总速率之间的权衡会导致非凸、多目标优化问题，且复杂度随网络规模增加而上升。为缓解这一矛盾，我们提出一种基于无线Transformer（WiT）架构的优化驱动无监督学习方法，从信道状态信息（CSI）特征中进行学习。通过拉格朗日乘子将总速率与公平性目标结合，并利用对偶上升算法自动更新该乘子，从而实现可调控的公平性约束，同时最大化总速率，有效在两个冲突目标之间逼近帕累托前沿。实验表明，该方法可在预设公平性条件下提供灵活的权衡优化解决方案。
原文摘要 · Abstract (English)
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.
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