[2603.03146] Channel-Adaptive Edge AI: Maximizing Inference Throughput by Adapting Computational Complexity to Channel States

Computer Science > Information Theory arXiv:2603.03146 (cs) [Submitted on 3 Mar 2026] Title:Channel-Adaptive Edge AI: Maximizing Inference Throughput by Adapting Computational Complexity to Channel States Authors:Jierui Zhang, Jianhao Huang, Kaibin Huang View a PDF of the paper titled Channel-Adaptive Edge AI: Maximizing Inference Throughput by Adapting Computational Complexity to Channel States, by Jierui Zhang and 2 other authors View PDF HTML (experimental) Abstract:\emph{Integrated communication and computation} (IC$^2$) has emerged as a new paradigm for enabling efficient edge inference in sixth-generation (6G) networks. However, the design of IC$^2$ technologies is hindered by the lack of a tractable theoretical framework for characterizing \emph{end-to-end} (E2E) inference performance. The metric is highly complicated as it needs to account for both channel distortion and artificial intelligence (AI) model architecture and computational complexity. In this work, we address this challenge by developing a tractable analytical model for E2E inference accuracy and leveraging it to design a \emph{channel-adaptive AI} algorithm that maximizes inference throughput, referred to as the edge processing rate (EPR), under latency and accuracy constraints. Specifically, we consider an edge inference system in which a server deploys a backbone model with early exit, which enables flexible computational complexity, to perform inference on data features transmitted by a mobile devi...