Robustness-Enabled Energy-Efficient Feature Transmission for Edge Inference

Deploying artificial intelligence (AI) algorithms at the network edge is essential for enabling next-generation network applications. In edge inference systems, features are extracted from distributed sensors and transmitted to an edge server for remote inference. However, traditional communication systems are primarily designed for reliable bit-level transmission and do not account for the unique characteristics of AI-based inference. A key property of AI inference is robustness: the ability to tolerate distortions in input features without significantly degrading performance. Leveraging this property, we propose a robustness-aware framework for energy-efficient edge inference that reduces energy consumption during feature transmission. By allowing nonzero bit error rates (BERs) in the transmitted features, our approach exploits the inherent robustness of AI models to reduce energy consumption while preserving inference accuracy. We begin by quantifying the robustness of inference using the classification margin and analyzing the relationship between classification accuracy and the average BER across distributed sensors. Based on this analysis, we formulate an optimization problem that seeks to minimize the total energy consumption of the sensors while satisfying classification accuracy requirements under radio resource constraints. To solve this problem, we decompose it into subproblems involving sensor selection, power control, and bandwidth allocation. These are efficiently addressed using a low-complexity alternating iterative algorithm. Simulation results demonstrate that the proposed robustness-aware inference framework can effectively tolerate bit errors during feature transmission, achieving significant energy reductions while maintaining target classification accuracy. Our design consistently outperforms benchmark schemes.