Energy-Aware Compression-Computation Co-Adaptation for Latency Minimization in Multi-User Semantic Communication

2026-08-13

AI Summary

This study addresses the challenges of Quality-of-Service (QoS) provisioning and energy consumption adaptation caused by device heterogeneity in multi-user semantic communication. We propose CoCo, a compression-computation collaborative adaptation framework that integrates retraining-free robust codecs, deep joint source-channel coding, and a greedy subcarrier allocation strategy. By decomposing parameter optimization, CoCo achieves efficient resource configuration tailored to heterogeneous devices. Experimental results demonstrate that CoCo significantly reduces total system latency compared to conventional methods while effectively balancing energy consumption under differentiated QoS requirements. Consequently, this framework provides a robust and efficient solution for semantic communication in heterogeneous environments, overcoming critical limitations in existing adaptive transmission schemes.

Abstract

Deep joint source-channel coding-enabled (DeepJSCC) semantic communication (SemCom) has excelled at delivering high perceptual quality at low channel-bandwidth ratios, which positions it as a pillar for next-generation wireless networks. However, the existing works have difficulty accommodating user heterogeneity in terms of communication channel quality, expected quality-of-service (QoS) targets, and the available local energy. Therefore, in this paper, we explicitly reflect the heterogeneity of user devices in terms of the differences in expected QoS, channel condition, and local energy, and then mathematically formulate the problem. Next, we propose an energy-aware compression-computation co-adaptation (CoCo) framework, in which the base station can meet the expected user QoS by transmitting a longer signal or offloading the task to a local device. The user has to dedicate energy to denoising the signal to recover higher-fidelity latent features before feeding it to the semantic decoder. To solve the formulated problem, we first decompose it into two sub-problems: parameter optimization and resource allocation problems. Specifically, we propose a robust codec that effectively works under a diversity of compression rates and channel noise without re-training, while the greedy sub-carrier allocation lowers the communication time. Finally, we present simulation results on standard image datasets over additive white Gaussian noise to demonstrate the effectiveness of CoCo, which reduces total latency relative to rate-only adaptive DeepJSCC or denoising-only, thereby ensuring the demands of each individual user are met.

Problem

• Semantic Communication
• User Heterogeneity
• Latency Minimization
• Energy-Aware
• DeepJSCC

Innovation

• Semantic Communication
• Compression-Computation Co-Adaptation
• Energy-Aware
• Robust Codec
• Latency Minimization