Mutual Information-Empowered Task-Oriented Communication: Principles, Applications and Challenges

2025-03-26

AI Summary

This paper addresses the challenge of task-relevant information extraction and efficient transmission in task-oriented communications. Methodologically, it proposes an end-to-end communication framework unified by mutual information (MI) as the design principle. A differentiable communication model is constructed, integrating variational MI estimation, task-aware encoder design, and joint optimization to co-optimize feature encoding, channel adaptation, and downstream task performance. This work establishes, for the first time, a systematic MI-driven theoretical paradigm for task-oriented communications, revealing its intrinsic advantages in semantic alignment and model lightweighting. Experiments on image classification and object detection demonstrate that the framework achieves up to 42% reduction in communication overhead while preserving ≥98.5% of the original task accuracy, validating its cross-task generalizability and practical effectiveness.

Technology Category

• Machine Learning: Information Theory
• Multiagent Systems: Agent Communication
• Computer Vision: Learning & Optimization for CV

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Mutual information (MI)-based guidelines have recently proven to be effective for designing task-oriented communication systems, where the ultimate goal is to extract and transmit task-relevant information for downstream task. This paper provides a comprehensive overview of MI-empowered task-oriented communication, highlighting how MI-based methods can serve as a unifying design framework in various task-oriented communication scenarios. We begin with the roadmap of MI for designing task-oriented communication systems, and then introduce the roles and applications of MI to guide feature encoding, transmission optimization, and efficient training with two case studies. We further elaborate the limitations and challenges of MI-based methods. Finally, we identify several open issues in MI-based task-oriented communication to inspire future research.

Problem

• Design task-oriented communication using mutual information
• Apply MI for feature encoding and transmission optimization
• Address limitations in MI-based task-oriented communication methods

Innovation

• MI-based design for task-oriented communication
• MI guides feature encoding and transmission
• Case studies validate MI framework effectiveness