Wireless for Machine Learning: A Survey

Henrik Hellström, José Mairton Barros da Silva, Mohammad Mohammadi Amiri, Mingzhe Chen, Viktoria Fodor, H. Vincent Poor, Carlo Fischione

Research output: Contribution to journalReview articlepeer-review

13 Scopus citations

Abstract

As data generation increasingly takes place on devices without a wired connection, Machine Learning (ML) related traffic will be ubiquitous in wireless networks. Many studies have shown that traditional wireless protocols are highly inefficient or unsustainable to support ML, which creates the need for new wireless communication methods. In this monograph, we give a comprehensive review of the state-of-the-art wireless methods that are specifically designed to support ML services over distributed datasets. Currently, there are two clear themes within the literature, analog over-the-air computation and digital radio resource management optimized for ML. This survey gives an introduction to these methods, reviews the most important works, highlights open problems, and discusses application scenarios.

Original languageEnglish (US)
Pages (from-to)290-399
Number of pages110
JournalFoundations and Trends in Signal Processing
Volume15
Issue number4
DOIs
StatePublished - Jun 9 2022

All Science Journal Classification (ASJC) codes

  • Signal Processing

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