Frequency Modulation for Task-Oriented Communications and Multiple Access

TL;DR

This paper explores the use of frequency modulation and type-based multiple access for federated edge learning, demonstrating improvements in convergence and power efficiency over existing methods.

Contribution

It introduces a novel modulation scheme combining FM and TMBA for FEEL, addressing a gap in AirComp waveform design.

Findings

Enhanced convergence performance

Reduced peak-to-average power ratio

Superior to existing AirComp schemes

Abstract

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).