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.

翻译：在无线通信中确保用户公平性是一个根本性挑战，因为平衡公平性与总速率之间的权衡会导致一个非凸、多目标优化问题，其复杂度随网络规模增长。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，该方法从信道状态信息（CSI）特征中学习。我们通过拉格朗日乘子将总速率与公平性目标相结合，从而重构该权衡问题，该乘子通过双上升算法自动更新。此机制在最大化总速率的同时允许可控的公平性约束，有效实现了两个冲突目标间帕累托前沿上的轨迹追踪。研究结果表明，所提方法为在预设公平性条件下管理权衡优化提供了灵活的解决方案。