论文标题
部分可观测时空混沌系统的无模型预测
Over-The-Air Federated Learning Over Scalable Cell-free Massive MIMO
论文作者
论文摘要
无细胞的大型MIMO正在成为未来无线通信系统的有前途的技术，与经典的蜂窝系统相比，预计将提供均匀的覆盖范围和高光谱效率。我们在本文中研究了无细胞的大型MIMO如何支持联合边缘学习。利用无线多访问频道的附加性质，可以利用无线计算，客户同时通过相同的通信资源发送本地更新。这种方法被称为空中联邦学习（OTA-FL），被证明是可以减轻无线网络联邦学习的沟通开销。考虑到中央服务器上可用的通道相关性和仅可用的通道状态信息，我们建议对无细胞的大型MIMO实现实际实现。在分析和实验上研究了所提出的实施的收敛性，从而证实了无细胞大量MIMO对OTA-FL的益处。
Cell-free massive MIMO is emerging as a promising technology for future wireless communication systems, which is expected to offer uniform coverage and high spectral efficiency compared to classical cellular systems. We study in this paper how cell-free massive MIMO can support federated edge learning. Taking advantage of the additive nature of the wireless multiple access channel, over-the-air computation is exploited, where the clients send their local updates simultaneously over the same communication resource. This approach, known as over-the-air federated learning (OTA-FL), is proven to alleviate the communication overhead of federated learning over wireless networks. Considering channel correlation and only imperfect channel state information available at the central server, we propose a practical implementation of OTA-FL over cell-free massive MIMO. The convergence of the proposed implementation is studied analytically and experimentally, confirming the benefits of cell-free massive MIMO for OTA-FL.
