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arXiv 2406.20007eess.SP

面向任务导向通信与多址接入的调频技术

Frequency Modulation for Task-Oriented Communications and Multiple Access

Marc Martinez-Gost, Ana Pérez-Neira, Miguel Ángel Lagunas

AI总结:

本文针对联邦边缘学习中的空中计算调制设计不足问题,提出将调频与基于类型的多址接入结合用于FEEL,在收敛性和峰均功率比上优于现有方法。

摘要:

在任务导向通信的背景下,我们倡导为联邦边缘学习(FEEL)开发波形。空中计算(AirComp)已成为一种通信方案,能够从分布式数据中计算函数,并可应用于FEEL。然而,针对AirComp的调制设计仍处于起步阶段,大多数文献忽视了这一主题。在这项工作中,我们将调频(FM)和基于类型的多址接入(TMBA)用于FEEL,并展示了其在收敛性和峰均功率比(PAPR)方面相对于现有技术的优势。

英文摘要:

In the context of task-oriented communications we advocate the development of waveforms for Federated Edge Learning (FEEL). Over-the-air computing (AirComp) has emerged as a communication scheme that allows to compute a function out of distributed data and can be applied to FEEL. However, the design of modulations for AirComp is still in its infancy and most of the literature ignores this topic. In this work we employ frequency modulation (FM) and type based multiple access (TMBA) for FEEL and demonstrate its advantages with respect to the state of the art in terms of convergence and peak-to-average power ratio (PAPR).

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。

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