Adaptive Unequal Error Protection for Semantic Split Learning over Wireless Channels

2026-08-17

AI Summary

This study addresses the challenge of adapting transmission reliability to task relevance in semantic segmentation for wireless edge cloud systems. To this end, we propose a task-aware learning framework that leverages mutual information gradients to establish a task prioritization mechanism. This approach enables fully learning-driven adaptive unequal error protection and end-to-end optimization of the communication interface. Experimental evaluations on real-world IoT datasets across varying signal-to-noise ratios demonstrate that the proposed framework significantly outperforms existing baselines. Furthermore, it exhibits superior generalization capability and robustness, effectively enhancing both semantic segmentation accuracy and transmission efficiency in resource-constrained scenarios.

Abstract

We propose a task-aware semantic split learning (SL) framework for wireless edge-cloud inference, in which the reliability of transmitted latent representations is dynamically adapted to their relevance for the downstream task. An autoencoder (AE)-based physical (PHY) layer enables end-to-end learning of the communication interface, while unequal error protection (UEP) is realized via mutual information (MI)-driven prioritization of latent components during training. The gradient of the estimated MI with respect to each latent component serves as a sensitivity-based proxy for task relevance, providing a fully learning-driven prioritization that adapts to both the data distribution and the downstream task. We further show that this prioritization translates into measurable physical-layer effects: MI-guided UEP assigns significantly higher transmit power to the most task-critical latent components compared to the equal error protection (EEP) baseline. Experiments on real-world IoT sensing data demonstrate consistent gains over equal and fixed-UEP baselines across SNR regimes. Additional analysis confirms ranking stability, estimator robustness and generalization across datasets and task types, indicating broad applicability of the proposed framework.

Problem

• Semantic Split Learning
• Wireless Edge-Cloud Inference
• Unequal Error Protection
• Task Relevance
• Latent Representations

Innovation

• Semantic Split Learning
• Unequal Error Protection
• Mutual Information
• Task-aware
• End-to-end Learning