机器联觉（SoM）辅助的具有感知异构性的频分双工预编码：一种纵向联邦学习方法
Synesthesia of Machines (SoM)-Aided FDD Precoding with Sensing Heterogeneity: A Vertical Federated Learning Approach
AI总结：
本文提出一种机器联觉辅助的纵向联邦学习预编码方案，利用多模态感知和在线训练策略，在缩短导频序列的同时逼近完美信道状态信息下的传统优化性能。
摘要：
频分双工系统中预编码设计的高复杂度需要精简的解决方案。在机器联觉（SoM）的指导下，本文在纵向联邦学习（VFL）框架内引入了一种异构多车辆、多模态感知辅助的预编码方案，该方案显著缩短了导频序列长度，同时优化了系统的和速率。我们通过精心设计的VFL训练流程，解决了由于车载传感器配置不同而导致的本地数据异构性带来的挑战。为了从多模态感知中提取有价值的信道特征，我们采用了三种不同的数据预处理方法，将原始数据转换为与预编码相关的信息表示。此外，我们提出了一种基于VFL框架的在线训练策略，使该方案能够动态适应用户数量的波动。数值结果表明，我们的方法在利用短导频序列的情况下，能够接近具有完美信道状态信息的传统优化方法的性能。
英文摘要：
High complexity in precoding design for frequency division duplex systems necessitates streamlined solutions. Guided by Synesthesia of Machines (SoM), this paper introduces a heterogeneous multi-vehicle, multi-modal sensing aided precoding scheme within a vertical federated learning (VFL) framework, which significantly minimizes pilot sequence length while optimizing the system's sum rate. We address the challenges posed by local data heterogeneity due to varying on-board sensor configurations through a meticulously designed VFL training procedure. To extract valuable channel features from multi-modal sensing, we employ three distinct data preprocessing methods that convert raw data into informative representations relevant for precoding. Additionally, we propose an online training strategy based on VFL framework, enabling the scheme to adapt dynamically to fluctuations in user numbers. Numerical results indicate that our approach, utilizing short pilot sequences, closely approximates the performance of traditional optimization methods with perfect channel state information.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
