Improving Channel Resilience for Task-Oriented Semantic Communications: A Unified Information Bottleneck Approach

TL;DR

This paper proposes a unified information bottleneck framework to improve the resilience of task-oriented semantic communications against channel variations, enhancing semantic robustness at the feature level.

Contribution

It introduces a novel framework that controls information flow to bolster semantic feature robustness in TSC under channel fading conditions.

Findings

Enhanced semantic robustness demonstrated in subchannel allocation tasks.

Framework effectively mitigates errors caused by channel variations.

Improved resource efficiency in semantic communications.

Abstract

Task-oriented semantic communications (TSC) enhance radio resource efficiency by transmitting task-relevant semantic information. However, current research often overlooks the inherent semantic distinctions among encoded features. Due to unavoidable channel variations from time and frequency-selective fading, semantically sensitive feature units could be more susceptible to erroneous inference if corrupted by dynamic channels. Therefore, this letter introduces a unified channel-resilient TSC framework via information bottleneck. This framework complements existing TSC approaches by controlling information flow to capture fine-grained feature-level semantic robustness. Experiments on a case study for real-time subchannel allocation validate the framework's effectiveness.