When Semantic Communication Meets Queueing: Cross-Layer Latency and Task Fidelity Optimization
📰 ArXiv cs.AI
Optimize cross-layer latency and task fidelity in semantic communication using queueing theory and multi-task semantic autoencoders
Action Steps
- Apply queueing theory to model the latency in semantic communication systems
- Use multi-task semantic autoencoders to jointly reconstruct images and predict task-relevant information
- Optimize the encoder-decoder architecture to minimize cross-layer latency and maximize task fidelity
- Evaluate the performance of the optimized system using metrics such as spectrum efficiency and task accuracy
- Implement the optimized system in a wireless communication network to improve the transmission of semantic images
Who Needs to Know This
Researchers and engineers working on semantic communication, queueing theory, and multi-task learning can benefit from this study to improve the efficiency of wireless communication systems
Key Insight
💡 Semantic communication with learned encoder-decoder architectures can be optimized using queueing theory to reduce latency and improve task fidelity
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📱💻 Optimizing semantic communication with queueing theory and multi-task learning! 🚀
Key Takeaways
Optimize cross-layer latency and task fidelity in semantic communication using queueing theory and multi-task semantic autoencoders
Full Article
Title: When Semantic Communication Meets Queueing: Cross-Layer Latency and Task Fidelity Optimization
Abstract:
arXiv:2605.05514v1 Announce Type: cross 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 pred
Abstract:
arXiv:2605.05514v1 Announce Type: cross 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 pred
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