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

Shuai Lyu, Yao Sun, Linke Guo, Xiaoyong Yuan, Fang Fang, Lan Zhang, Xianbin Wang

TL;DR

The paper tackles the challenge of channel-level distortions in task-oriented semantic communications (TSC) by introducing a unified channel-resilience framework grounded in the information bottleneck (IB). It freezes the existing encoder/decoder and uses artificial noise to estimate per-feature robustness, deriving an upper-bound objective $L(\sigma)$ and a per-feature robustness mask $r_k$ that guides transmission decisions. The IB-based robustness estimation yields a soft mask that prioritizes semantically important, resilient feature units, demonstrated through a real-time subchannel allocation case study showing improved performance under dynamic channel conditions. This approach enhances radio-resource efficiency by enabling task-specific semantic inference to remain reliable despite instantaneous channel variations.

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.

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

TL;DR

The paper tackles the challenge of channel-level distortions in task-oriented semantic communications (TSC) by introducing a unified channel-resilience framework grounded in the information bottleneck (IB). It freezes the existing encoder/decoder and uses artificial noise to estimate per-feature robustness, deriving an upper-bound objective and a per-feature robustness mask that guides transmission decisions. The IB-based robustness estimation yields a soft mask that prioritizes semantically important, resilient feature units, demonstrated through a real-time subchannel allocation case study showing improved performance under dynamic channel conditions. This approach enhances radio-resource efficiency by enabling task-specific semantic inference to remain reliable despite instantaneous channel variations.

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.
Paper Structure (11 sections, 9 equations, 3 figures, 1 algorithm)

This paper contains 11 sections, 9 equations, 3 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model
  3. Channel-Resilient TSC Framework
  4. Design Intuition
  5. Information Bottleneck Reformulation
  6. Upper Bound of the IB-based Robustness Estimation
  7. Robustness Mask and Case Study
  8. Numerical Evaluation
  9. Evaluation Setup
  10. Evaluation Results
  11. Conclusion

Figures (3)

  • Figure 1: Overview of channel-resilient TSC framework. Blue arrows indicate IB-based channel-resilient analysis; black arrows indicate transmission procedures in the TSC system.
  • Figure 2: Comparison of feature-level channel resilience between the ideal (noise = 0) and noisy (SNR = 0) channel conditions.
  • Figure 3: Comparison of inference performance between dynamic (high variation, 15) and stable (low variation, 2) subchannel environments on CIFAR-10 and SVHN tasks.