用深度学习解决大规模无小区网络中用户与基站的高效关联问题。
A General Framework for Scalable UE-AP Association in User-Centric Cell-Free Massive MIMO based on Recurrent Neural Networks
- 采用双向LSTM结合概率权重更新，实现动态关联
- 支持用户和基站数量变化，无需重新训练
- 有效缓解导频污染问题，适合大规模无线网络
本文针对无小区大规模MIMO网络中的接入点（AP）与用户设备（UE）关联问题，提出一种基于双向长短期记忆网络的深度学习算法，并结合混合概率权重更新方法。该方法可适应用户数量变化而无需重新训练，显著提升系统可扩展性。此外，提出的训练策略进一步增强了对用户数和接入点数变化的适应能力。所提算法的变体还具备对抗导频污染的能力，有效缓解因导频复用导致的信道估计误差。大量数值实验验证了该方法的有效性和鲁棒性，其性能优于广泛应用的启发式算法。
原文摘要 · Abstract (English)
This study addresses the challenge of access point (AP) and user equipment (UE) association in cell-free massive MIMO networks. It introduces a deep learning algorithm leveraging Bidirectional Long Short-Term Memory cells and a hybrid probabilistic methodology for weight updating. This approach enhances scalability by adapting to variations in the number of UEs without requiring retraining. Additionally, the study presents a training methodology that improves scalability not only with respect to the number of UEs but also to the number of APs. Furthermore, a variant of the proposed AP-UE algorithm ensures robustness against pilot contamination effects, a critical issue arising from pilot reuse in channel estimation. Extensive numerical results validate the effectiveness and adaptability of the proposed methods, demonstrating their superiority over widely used heuristic alternatives.
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