用Transformer模型实现动态无线网络的智能功率分配，兼顾公平与效率。
Transformer-Based Power Optimization for Max-Min Fairness in Cell-Free Massive MIMO
- 基于用户和基站位置，用Transformer联合预测上下行最优功率
- 模型在不同用户数和基站数下表现接近最优，无需重新训练
- 适合动态变化的大型无线网络，部署灵活且计算开销低
功率分配是无线通信网络中的关键任务。传统优化算法和深度学习方法在小规模静态场景中有效，但在用户负载动态变化的大规模网络中，要么计算复杂度高，要么不适用。本文探索基于Transformer的深度学习模型解决该问题的潜力，提出一种神经网络，仅凭用户与接入点的位置信息，联合预测上行和下行链路的最优功率分配，以实现无蜂窝大规模多输入多输出系统中的最大最小公平性。数值结果表明，训练后的模型在不同用户数量和接入点数量下均能保持近似最优性能，且无需重训练、额外处理或修改网络结构。这验证了所提模型在动态网络中实现鲁棒、灵活功率分配的有效性。
原文摘要 · Abstract (English)
Power allocation is an important task in wireless communication networks. Classical optimization algorithms and deep learning methods, while effective in small and static scenarios, become either computationally demanding or unsuitable for large and dynamic networks with varying user loads. This letter explores the potential of transformer-based deep learning models to address these challenges. We propose a transformer neural network to jointly predict optimal uplink and downlink power using only user and access point positions. The max-min fairness problem in cell-free massive multiple input multiple output systems is considered. Numerical results show that the trained model provides near-optimal performance and adapts to varying numbers of users and access points without retraining, additional processing, or updating its neural network architecture. This demonstrates the effectiveness of the proposed model in achieving robust and flexible power allocation for dynamic networks.
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