When Semantic Communication Meets Queueing: Cross-Layer Latency and Task Fidelity Optimization

2026-05-06

AI Summary

This work addresses the challenge of jointly optimizing task fidelity, latency, and spectral efficiency in semantic communications by proposing a multi-task semantic autoencoder framework for image transmission. The approach treats the latent space dimensionality as a semantic rate control variable and employs a drift-plus-penalty strategy to jointly optimize image reconstruction and label prediction over block Rayleigh fading and AWGN channels. A novel online semantic rate control mechanism, driven by queue state and Age of Information (AoI), is introduced to enable dynamic cross-layer trade-offs. Experimental results demonstrate that, compared to fixed-rate schemes, the proposed method significantly reduces end-to-end latency and AoI while improving spectral efficiency and timeliness, all under a long-term semantic error constraint.

Abstract

Semantic communication (SemCom) with learned encoder-decoder architectures enables end-to-end learning of compact task-oriented representations optimized for the wireless channel, reducing channel resources needed to convey task-relevant information and improving spectrum efficiency. This paper studies semantic image transmission over block Rayleigh fading with AWGN using a multi-task semantic autoencoder that jointly reconstructs images and predicts labels from the received waveform. The latent dimension (complex channel uses per source sample) serves as a cross-layer control variable governing semantic fidelity and channel resource usage. We characterize the resulting latency-task fidelity tradeoff: larger latent representations improve inference accuracy but increase service time, channel uses, and queueing delay. Building on this insight, we develop online semantic-rate controllers that adapt the latent dimension per update under a long-term semantic error constraint. A queue-aware drift-plus-penalty policy minimizes delay subject to an average semantic error cap, while a complementary age-aware policy minimizes time-average Age of Information (AoI). By adapting the semantic rate to congestion and fidelity requirements, the proposed framework improves spectrum utilization and enables timely semantic updates with significantly lower delay and AoI than fixed-rate baselines.

Problem

• Semantic Communication
• Latency Optimization
• Task Fidelity
• Queueing Delay
• Age of Information

Innovation

• Semantic Communication
• Cross-layer Optimization
• Latent Dimension Adaptation
• Queueing Delay
• Age of Information