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

TL;DR

This paper surveys mutual information (MI)-based methods for task-oriented communication, arguing that MI provides a principled, end-to-end framework to align encoding, transmission, and learning toward task relevance. It unifies design via a three-part pipeline: probabilistic modeling with PGMs/Venn diagrams, MI-based objective functions, and learning algorithms for MI estimation, enabling robust feature encoding, efficient transmission, and training efficiency. Key contributions include the presentation of IB, DIB, IIB, and Graph IB variants; MI-guided robust modulation and MIMO precoding; MI-based training frameworks and cross-transceiver alignment; and a candid discussion of challenges (high-dimensional MI, invariance) and practical remedies. The work highlights future directions in multi-task and multi-modal learning and power control, underscoring MI’s potential to enhance efficiency, robustness, and adaptability in next-generation intelligent communication systems.

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.

Figures (5)

• Figure 1: Three fundamental components of MI-based task-oriented communication system design.
• Figure 2: The roles, applications, and challenges of MI-based task-oriented communication.
• Figure 3: The PGM of the IFE-based task-oriented communication system.
• Figure 4: The causal feature inference accuracy on Colored-MNIST of different methods. All methods are trained on $\textrm{PSNR}_{\textrm{train}}=15\textrm{ dB}$.
• Figure 5: The cross-transceiver task-oriented communication performance (a) without any feature alignment (Non-alignment) and (b) with receiver-side feature alignment (Receiver-side alignment) under the AWGN channels with $\textrm{SNR} = 10\textrm{ dB}$ on the SVHN dataset.