用深度学习解决无蜂窝大规模MIMO中用户聚类问题，提升系统效率与公平性。
A Deep Learning Approach for User-Centric Clustering in Cell-Free Massive MIMO Systems
- 基于LSTM的深度学习方法，无需重训练即可扩展至更多用户。
- 在不完美信道信息下仍保持高效，显著提升频谱效率。
- 适合需要高并发、低延迟通信的未来移动网络场景。
与传统存在小区间干扰的大规模MIMO蜂窝配置不同，无蜂窝大规模MIMO将网络资源分布于覆盖区域，使用户可连接多个接入点（AP），从而提升系统容量和用户公平性。在此类系统中，接入点与用户间的关联是关键功能：确定最优关联是一个组合复杂度极高的问题。本文提出一种基于深度学习的解决方案，用于最大化总频谱效率的同时控制活跃连接数。该方法能有效扩展至大量用户，利用长短期记忆单元实现无需重训练的运行。数值结果表明，即使在存在导频污染导致的信道状态信息不完善情况下，该方案仍具有效性。
原文摘要 · Abstract (English)
Contrary to conventional massive MIMO cellular configurations plagued by inter-cell interference, cell-free massive MIMO systems distribute network resources across the coverage area, enabling users to connect with multiple access points (APs) and boosting both system capacity and fairness across user. In such systems, one critical functionality is the association between APs and users: determining the optimal association is indeed a combinatorial problem of prohibitive complexity. In this paper, a solution based on deep learning is thus proposed to solve the user clustering problem aimed at maximizing the sum spectral efficiency while controlling the number of active connections. The proposed solution can scale effectively with the number of users, leveraging long short-term memory cells to operate without the need for retraining. Numerical results show the effectiveness of the proposed solution, even in the presence of imperfect channel state information due to pilot contamination.
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