Learning-based downlink power control in cell-free massive multiple-input multiple-output (CFmMIMO) systems offers a promising alternative to conventional iterative optimization algorithms, which are computationally intensive due to online iterative steps. Existing learning-based methods, however, often fail to exploit the intrinsic structure of channel data and neglect pilot allocation information, leading to suboptimal performance, especially in large-scale networks with many users. This paper introduces the pilot contamination-aware power control (PAPC) transformer neural network, a novel approach that integrates pilot allocation data into the network, effectively handling pilot contamination scenarios. PAPC employs the attention mechanism with a custom masking technique to utilize structural information and pilot data. The architecture includes tailored preprocessing and post-processing stages for efficient feature extraction and adherence to power constraints. Trained in an unsupervised learning framework, PAPC is evaluated against the accelerated proximal gradient (APG) algorithm, showing comparable spectral efficiency fairness performance while significantly improving computational efficiency. Simulations demonstrate PAPC's superior performance over fully connected networks (FCNs) that lack pilot information, its scalability to large-scale CFmMIMO networks, and its computational efficiency improvement over APG. Additionally, by employing padding techniques, PAPC adapts to the dynamically varying number of users without retraining.

翻译：基于学习的无蜂窝大规模多输入多输出（CFmMIMO）系统下行功率控制为传统迭代优化算法提供了一种有前景的替代方案，后者因在线迭代步骤而计算密集。然而，现有基于学习的方法通常未能充分利用信道数据的内在结构，且忽略了导频分配信息，导致性能欠佳，尤其是在具有众多用户的大规模网络中。本文提出了导频污染感知功率控制（PAPC）Transformer神经网络，这是一种将导频分配数据整合到网络中的新方法，能有效处理导频污染场景。PAPC采用带有自定义掩码技术的注意力机制来利用结构信息和导频数据。该架构包含定制的预处理和后处理阶段，以实现高效的特征提取并满足功率约束。PAPC在无监督学习框架下训练，通过与加速近端梯度（APG）算法对比评估，显示出相当的信道容量公平性性能，同时显著提高了计算效率。仿真结果表明，PAPC性能优于缺乏导频信息的全连接网络（FCN），能扩展到大规模CFmMIMO网络，且计算效率较APG有所提升。此外，通过采用填充技术，PAPC能够适应动态变化的用户数量而无需重新训练。