# Adaptive Bit Rate Control in Semantic Communication with Incremental Knowledge-based HARQ

arXiv: https://arxiv.org/abs/2203.06634 · HTML: https://mlsanity.com/paper/2203.06634/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Mar 2022.

## Built on by (most cited first, 11 of 11)

- [Digital-SC: Digital Semantic Communication with Adaptive Network Split and Learned Non-Linear Quantization](https://mlsanity.com/paper/2305.13553/) (2023)
- [Semantics-Empowered Communication: A Tutorial-cum-Survey](https://mlsanity.com/paper/2212.08487/) (2022)
- [Semantic Edge Computing and Semantic Communications in 6G Networks: A Unifying Survey and Research Challenges](https://mlsanity.com/paper/2411.18199/) (2024)
- [Knowledge Enhanced Semantic Communication Receiver](https://mlsanity.com/paper/2302.07727/) (2023)
- [Semantic Communication for Cooperative Perception using HARQ](https://mlsanity.com/paper/2409.09042/) (2024)
- [In-Context Source and Channel Coding](https://mlsanity.com/paper/2601.10267/) (2026)
- [Generative Semantic HARQ: Latent-Space Text Retransmission and Combining](https://mlsanity.com/paper/2603.15068/) (2026)
- [Interplay of Semantic Communication and Knowledge Learning](https://mlsanity.com/paper/2402.03339/) (2024)
- [Knowledge Abstraction for Knowledge-based Semantic Communication: A Generative Causality Invariant Approach](https://mlsanity.com/paper/2507.17784/) (2025)
- [Contextual Memory-Enhanced Source Coding for Low-SNR Communications](https://mlsanity.com/paper/2605.04400/) (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)

Semantic communication has witnessed a great progress with the development of natural language processing (NLP) and deep learning (DL). Although existing semantic communication technologies can effectively reduce errors in semantic interpretation, most of these solutions adopt a fixed bit length structure, along with a rigid transmission scheme, which is inefficient and lacks scalability when faced with different meanings and signal-to-noise ratio (SNR) conditions. In this paper, we explore the impact of adaptive bit lengths on semantic coding (SC) under various channel conditions. First, we propose progressive semantic hybrid automatic repeat request (HARQ) schemes that utilize incremental knowledge (IK) to simultaneously reduce the communication cost and semantic error. On top of this, we design a novel semantic encoding solution with multi-bit length selection. In this fashion, the transmitter employs a policy network to decide the appropriate coding rate, so as to secure the correct information delivery at the cost of minimal bits. Moreover, a specific denoiser is further introduced to reduce the semantic errors encountered in the transmission process according to the semantic characteristics of context. Extensive simulation results have been conducted to verify the effectiveness of the proposed solution.