A PAC-Bayesian Analysis of Channel-Induced Degradation in Edge Inference

• URL: http://arxiv.org/abs/2601.10915v1
• Date: Fri, 16 Jan 2026 00:10:17 GMT
• Title: A PAC-Bayesian Analysis of Channel-Induced Degradation in Edge Inference
• Authors: Yangshuo He, Guanding Yu, Jingge Zhu,
• Abstract summary: We introduce an augmented NN model that incorporates channel statistics directly into the weight space.<n>We derive PAC-Bayesian generalization bounds that explicitly quantifies the impact of wireless distortion.<n>We propose a channel-aware training algorithm that minimizes a surrogate objective based on the derived bound.
• Score: 22.97773242617207
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the emerging paradigm of edge inference, neural networks (NNs) are partitioned across distributed edge devices that collaboratively perform inference via wireless transmission. However, standard NNs are generally trained in a noiseless environment, creating a mismatch with the noisy channels during edge deployment. In this paper, we address this issue by characterizing the channel-induced performance deterioration as a generalization error against unseen channels. We introduce an augmented NN model that incorporates channel statistics directly into the weight space, allowing us to derive PAC-Bayesian generalization bounds that explicitly quantifies the impact of wireless distortion. We further provide closed-form expressions for practical channels to demonstrate the tractability of these bounds. Inspired by the theoretical results, we propose a channel-aware training algorithm that minimizes a surrogate objective based on the derived bound. Simulations show that the proposed algorithm can effectively improve inference accuracy by leveraging channel statistics, without end-to-end re-training.

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