# Deep Source-Channel Coding for Sentence Semantic Transmission with HARQ

arXiv: https://arxiv.org/abs/2106.03009 · HTML: https://mlsanity.com/paper/2106.03009/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jun 2021.

## Built on by (most cited first, 29 of 29)

- [Semantic Communications: Principles and Challenges](https://mlsanity.com/paper/2201.01389/) (2021)
- [Robust Semantic Communications with Masked VQ-VAE Enabled Codebook](https://mlsanity.com/paper/2206.04011/) (2022)
- [Task-Oriented Image Transmission for Scene Classification in Unmanned Aerial Systems](https://mlsanity.com/paper/2112.10948/) (2021)
- [Learning Semantics: An Opportunity for Effective 6G Communications](https://mlsanity.com/paper/2110.08049/) (2021)
- [Joint Task and Data Oriented Semantic Communications: A Deep Separate Source-channel Coding Scheme](https://mlsanity.com/paper/2302.13580/) (2023)
- [Reinforcement Learning-powered Semantic Communication via Semantic Similarity](https://mlsanity.com/paper/2108.12121/) (2021)
- [Semantics-Empowered Communication: A Tutorial-cum-Survey](https://mlsanity.com/paper/2212.08487/) (2022)
- [A Wireless AI-Generated Content (AIGC) Provisioning Framework Empowered by Semantic Communication](https://mlsanity.com/paper/2310.17705/) (2023)
- [Importance-Aware Image Segmentation-based Semantic Communication for Autonomous Driving](https://mlsanity.com/paper/2401.10153/) (2024)
- [Synchronous Multi-modal Semantic Communication System with Packet-level Coding](https://mlsanity.com/paper/2408.04535/) (2024)
- [Learning Based Joint Coding-Modulation for Digital Semantic Communication Systems](https://mlsanity.com/paper/2208.05704/) (2022)
- [Deep Learning in Physical Layer: Review on Data Driven End-to-End Communication Systems and their Enabling Semantic Applications](https://mlsanity.com/paper/2401.12800/) (2024)
- [Adaptive Information Bottleneck Guided Joint Source and Channel Coding for Image Transmission](https://mlsanity.com/paper/2203.06492/) (2022)
- [Semantic Edge Computing and Semantic Communications in 6G Networks: A Unifying Survey and Research Challenges](https://mlsanity.com/paper/2411.18199/) (2024)
- [Deep Learning-Empowered Semantic Communication Systems with a Shared Knowledge Base](https://mlsanity.com/paper/2311.02884/) (2023)
- [Knowledge Enhanced Semantic Communication Receiver](https://mlsanity.com/paper/2302.07727/) (2023)
- [Trustworthy Image Semantic Communication with GenAI: Explainablity, Controllability, and Efficiency](https://mlsanity.com/paper/2408.03806/) (2024)
- [A Survey on Semantic Communications in Internet of Vehicles](https://mlsanity.com/paper/2503.03767/) (2025)
- [Economics of Semantic Communication System: An Auction Approach](https://mlsanity.com/paper/2208.05040/) (2022)
- [Learned Image Transmission with Hierarchical Variational Autoencoder](https://mlsanity.com/paper/2408.16340/) (2024)
- [Semantic Communication for Cooperative Perception using HARQ](https://mlsanity.com/paper/2409.09042/) (2024)
- [Semantic-Native Communication: A Simplicial Complex Perspective](https://mlsanity.com/paper/2210.16970/) (2022)
- [Modeling and Performance Analysis for Semantic Communications Based on Empirical Results](https://mlsanity.com/paper/2504.21055/) (2025)
- [Generative Semantic HARQ: Latent-Space Text Retransmission and Combining](https://mlsanity.com/paper/2603.15068/) (2026)
- [Transceiver Cooperative Learning-aided Semantic Communications Against Mismatched Background Knowledge Bases](https://mlsanity.com/paper/2301.03133/) (2023)
- [Resource Allocation for Semantic Communication under Physical-layer Security](https://mlsanity.com/paper/2312.04155/) (2023)
- [Interplay of Semantic Communication and Knowledge Learning](https://mlsanity.com/paper/2402.03339/) (2024)
- [Communication-Efficient Multi-Modal Edge Inference via Uncertainty-Aware Distributed Learning](https://mlsanity.com/paper/2601.14942/) (2026)
- [Embodied Semantic Communication for Collective Autonomous Agents: A Tutorial on Representation, Wireless Delivery, and Closed-Loop Coordination](https://mlsanity.com/paper/2609.35936/) (2026)

## Abstract (arXiv)

Recently, semantic communication has been brought to the forefront because of its great success in deep learning (DL), especially Transformer. Even if semantic communication has been successfully applied in the sentence transmission to reduce semantic errors, existing architecture is usually fixed in the codeword length and is inefficient and inflexible for the varying sentence length. In this paper, we exploit hybrid automatic repeat request (HARQ) to reduce semantic transmission error further. We first combine semantic coding (SC) with Reed Solomon (RS) channel coding and HARQ, called SC-RS-HARQ, which exploits the superiority of the SC and the reliability of the conventional methods successfully. Although the SC-RS-HARQ is easily applied in the existing HARQ systems, we also develop an end-to-end architecture, called SCHARQ, to pursue the performance further. Numerical results demonstrate that SCHARQ significantly reduces the required number of bits for sentence semantic transmission and sentence error rate. Finally, we attempt to replace error detection from cyclic redundancy check to a similarity detection network called Sim32 to allow the receiver to reserve the wrong sentences with similar semantic information and to save transmission resources.