Integrated Sensing and Communication for 6G: Ten Key Machine Learning Roles

TL;DR

This paper explores ten key machine learning roles in integrated sensing and communication for 6G, demonstrating real-world results with the DeepSense 6G dataset and outlining future research directions.

Contribution

It identifies and explains ten novel machine learning roles for joint sensing and communication, supported by large-scale real-world experimental results.

Findings

Successful application of ML roles on DeepSense 6G dataset

Enhanced performance in sensing and communication tasks

Guidelines for future integrated 6G system research

Abstract

Integrating sensing and communication is a defining theme for future wireless systems. This is motivated by the promising performance gains, especially as they assist each other, and by the better utilization of the wireless and hardware resources. Realizing these gains in practice, however, is subject to several challenges where leveraging machine learning can provide a potential solution. This article focuses on ten key machine learning roles for joint sensing and communication, sensing-aided communication, and communication-aided sensing systems, explains why and how machine learning can be utilized, and highlights important directions for future research. The article also presents real-world results for some of these machine learning roles based on the large-scale real-world dataset DeepSense 6G, which could be adopted in investigating a wide range of integrated sensing and communication problems.