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

arXiv: https://arxiv.org/abs/2605.05514 · HTML: https://mlsanity.com/paper/2605.05514/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 13 references that are in the map.
- First posted on arXiv 6 May 2026.

## Builds on (most cited first, 10 of 10)

- [Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communications](https://mlsanity.com/paper/2207.09353/) (2022)
- [Wireless Image Retrieval at the Edge](https://mlsanity.com/paper/2007.10915/) (2020)
- [Generative Joint Source-Channel Coding for Semantic Image Transmission](https://mlsanity.com/paper/2211.13772/) (2022)
- [Semantic Communications for Image Recovery and Classification via Deep Joint Source and Channel Coding](https://mlsanity.com/paper/2304.02317/) (2023)
- [Will 6G be Semantic Communications? Opportunities and Challenges from Task Oriented and Secure Communications to Integrated Sensing](https://mlsanity.com/paper/2401.01531/) (2024)
- [Energy Efficient Semantic Communication over Wireless Networks with Rate Splitting](https://mlsanity.com/paper/2301.01987/) (2023)
- [Age of Information in Deep Learning-Driven Task-Oriented Communications](https://mlsanity.com/paper/2301.04298/) (2023)
- [Joint Sensing and Task-Oriented Communications with Image and Wireless Data Modalities for Dynamic Spectrum Access](https://mlsanity.com/paper/2312.13931/) (2023)
- [Low-Latency Task-Oriented Communications with Multi-Round, Multi-Task Deep Learning](https://mlsanity.com/paper/2411.10385/) (2024)
- [On the Computing and Communication Tradeoff in Reasoning-Based Multi-User Semantic Communications](https://mlsanity.com/paper/2406.15199/) (2024)

## Abstract (arXiv)

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.