用用户位置信息联合优化基站分组与功率，提升系统效率。
Linear Attention for Joint Power Optimization and User-Centric Clustering in Cell-Free Networks
- 仅凭用户与基站位置，联合预测分组和功率分配
- 线性注意力机制使计算复杂度随用户数线性增长
- 无需信道估计，可避免导频污染，适合动态网络
在用户中心的无蜂窝大规模MIMO系统中，最优接入点（AP）分组与功率分配至关重要。现有深度学习模型难以适应动态网络配置，且多数方法忽略导频污染问题，计算开销高。本文提出一种轻量级Transformer模型，仅根据用户设备与AP的空间坐标，联合预测AP分组与功率分配。该模型对用户负载架构无关，无需信道估计开销，通过导频复用约束消除导频污染。引入定制化线性注意力机制，高效捕捉用户-基站交互，实现用户数线性可扩展性。数值结果表明，该模型能最大化最小频谱效率，在动态场景中实现接近最优性能，兼具适应性与可扩展性。
原文摘要 · Abstract (English)
Optimal AP clustering and power allocation are critical in user-centric cell-free massive MIMO systems. Existing deep learning models lack flexibility to handle dynamic network configurations. Furthermore, many approaches overlook pilot contamination and suffer from high computational complexity. In this paper, we propose a lightweight transformer model that overcomes these limitations by jointly predicting AP clusters and powers solely from spatial coordinates of user devices and AP. Our model is architecture-agnostic to users load, handles both clustering and power allocation without channel estimation overhead, and eliminates pilot contamination by assigning users to AP within a pilot reuse constraint. We also incorporate a customized linear attention mechanism to capture user-AP interactions efficiently and enable linear scalability with respect to the number of users. Numerical results confirm the model's effectiveness in maximizing the minimum spectral efficiency and providing near-optimal performance while ensuring adaptability and scalability in dynamic scenarios.
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