AI RESEARCH
When Semantic Communication Meets Queueing: Cross-Layer Latency and Task Fidelity Optimization
arXiv CS.LG
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ArXi:2605.05514v1 Announce Type: cross 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.