Measuring Discrete Sensing Capability for ISAC via Task Mutual Information

TL;DR

This paper introduces a theoretical framework for assessing discrete sensing capabilities in 6G systems using task mutual information, validated through real-case studies and showing high correlation with traditional metrics.

Contribution

It proposes a novel channel model and metric for performance evaluation of discrete sensing systems, enabling better system design and understanding.

Findings

High correlation (Pearson > 0.9) between task mutual information and accuracy.

Validation through diverse real-case sensing scenarios.

Theoretical insights into the impact of modalities and accuracy in wireless sensing.

Abstract

6G technology offers a broader range of possibilities for communication systems to perform ubiquitous sensing tasks, including health monitoring, object recognition, and autonomous driving. Since even minor environmental changes can significantly degrade system performance, and conducting long-term posterior experimental evaluations in all scenarios is often infeasible, it is crucial to perform a priori performance assessments to design robust and reliable systems. In this paper, we consider a discrete ubiquitous sensing system where the sensing target has \(m\) different states \(W\), which can be characterized by \(n\)-dimensional independent features \(X^n\). This model not only provides the possibility of optimizing the sensing systems at a finer granularity and balancing communication and sensing resources, but also provides theoretical explanations for classical intuitive feelings (like more modalities and more accuracy) in wireless sensing. Furthermore, we validate the effectiveness of the proposed channel model through real-case studies, including person identification, displacement detection, direction estimation, and device recognition. The evaluation results indicate a Pearson correlation coefficient exceeding 0.9 between our task mutual information and conventional experimental metrics (e.g., accuracy). The open source address of the code is: https://github.com/zaoanhh/DTMI