用树结构+Transformer实现大规模用户功率分配，速度快且效果好
Tree Meets Transformer: A Hybrid Architecture for Scalable Power Allocation in Cell-Free Networks
- 用二叉树压缩用户特征，只对根节点做Transformer编码
- 推理时间远低于全注意力模型，接近最优公平性表现
- 适合动态用户数变化的超大规模无线网络场景
功率分配是无线通信网络中的核心挑战，尤其在用户负载动态变化和大规模部署时。尽管基于Transformer的模型表现优异，但其计算开销随用户数呈平方增长。本文提出一种新型树-变压器混合架构，实现可扩展的逐用户功率分配。模型通过二叉树将用户特征压缩为全局根表示，仅对根节点应用Transformer编码器，并通过共享解码器恢复每个用户的上行与下行功率。该设计使深度为对数级，总复杂度线性，可在不重新训练或修改架构的前提下，高效处理大规模且变化的用户集合。我们在无蜂窝大规模MIMO系统的最大最小公平性问题上评估该模型，结果表明其性能接近最优，同时显著降低推理时间，优于全注意力基线。
原文摘要 · Abstract (English)
Power allocation remains a fundamental challenge in wireless communication networks, particularly under dynamic user loads and large-scale deployments. While Transformerbased models have demonstrated strong performance, their computational cost scales poorly with the number of users. In this work, we propose a novel hybrid Tree-Transformer architecture that achieves scalable per-user power allocation. Our model compresses user features via a binary tree into a global root representation, applies a Transformer encoder solely to this root, and decodes per-user uplink and downlink powers through a shared decoder. This design achieves logarithmic depth and linear total complexity, enabling efficient inference across large and variable user sets without retraining or architectural changes. We evaluate our model on the max-min fairness problem in cellfree massive MIMO systems and demonstrate that it achieves near-optimal performance while significantly reducing inference time compared to full-attention baselines.
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