TOIB: Task-Oriented Orthogonalised Information Bottleneck for Distributed Semantic Communication

TL;DR

This paper introduces TOIB, a novel task-oriented orthogonalised information bottleneck framework for distributed semantic communication, improving robustness and reducing interference in multi-user wireless networks.

Contribution

The paper proposes a new TOIB method that incorporates task-conditioned variables to address cross-user interference and optimize semantic transmission in distributed systems.

Findings

TOIB outperforms traditional IB and JSCC in classification accuracy.

TOIB enhances robustness under low-SNR conditions.

TOIB effectively suppresses cross-user semantic interference.

Abstract

Task-oriented semantic communication emerges as a crucial paradigm for next-generation wireless networks, aiming to efficiently transmit task-relevant information while reducing interference and redundancy across multiple users. Existing information bottleneck (IB)-based frameworks predominantly focus on single-user scenarios, neglecting cross-user semantic interference in distributed semantic communications. To overcome this limitation, we propose a task-oriented orthogonalised information bottleneck (TOIB) approach, explicitly designed for distributed semantic communication systems. By introducing task-conditioned latent variables, TOIB adaptively balances semantic sufficiency, semantic compression, and inter-user semantic orthogonality. Extensive simulations conducted on classification tasks demonstrate that TOIB consistently achieves superior classification accuracy across various signal-to-noise ratio (SNR) regimes compared to traditional IB and deep joint source-channel coding (JSCC) methods. Specifically, the proposed method significantly enhances robustness under harsh low-SNR conditions and effectively suppresses cross-user semantic interference, as validated by cross-decoding accuracy metrics.