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.

翻译：在用户中心化无蜂窝大规模MIMO系统中，最优接入点聚类与功率分配至关重要。现有深度学习模型缺乏处理动态网络配置的灵活性，且多数方法忽视导频污染问题并存在高计算复杂度。本文提出一种轻量级Transformer模型，仅通过用户设备与接入点的空间坐标即可联合预测接入点聚类与功率分配，从而克服上述局限。该模型对用户负载具有架构无关性，无需信道估计开销即可同时处理聚类与功率分配任务，并通过在导频复用约束下为用户分配接入点来消除导频污染。我们还引入定制化的线性注意力机制，以高效捕获用户-接入点交互关系，并实现随用户数量线性扩展的可扩展性。数值结果证实，该模型在动态场景中能有效提升最低频谱效率，在确保适应性与可扩展性的同时提供接近最优的性能表现。