Measuring Discrete Sensing Capability for ISAC via Task Mutual Information

2024-05-15

AI Summary

To address the lack of quantifiable performance metrics for discrete sensing tasks (e.g., human identification, displacement detection) in sixth-generation (6G) integrated sensing and communication (ISAC) systems, this paper proposes **Task Mutual Information (TMI)** as a universal information-theoretic measure of sensing capability. We are the first to incorporate TMI into an ISAC resource co-optimization framework, developing a discrete-state sensing channel model grounded in independent feature assumptions and enabling fine-grained joint optimization of communication and sensing resources. Experiments across four representative discrete sensing tasks demonstrate that TMI achieves Pearson correlation coefficients with task accuracy exceeding 0.9—significantly outperforming conventional metrics. Moreover, TMI provides a rigorous theoretical foundation for empirically observed phenomena, such as multimodal fusion improving sensing accuracy. The implementation code is publicly available.

Technology Category

• Machine Learning: Information Theory
• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Security and Privacy: Large-scale security measurements

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

Problem

• Assess sensing performance a priori for robust 6G systems
• Optimize discrete sensing systems with multi-state targets
• Validate model via real-case studies and high correlation metrics

Innovation

• Discrete sensing system with multi-state targets
• Task mutual information for performance assessment
• Validated via real-case studies and high correlation