# SCAN: Semantic Communication with Adaptive Channel Feedback

arXiv: https://arxiv.org/abs/2306.15534 · HTML: https://mlsanity.com/paper/2306.15534/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jun 2023.

## Built on by (most cited first, 14 of 14)

- [Robust Image Semantic Coding with Learnable CSI Fusion Masking over MIMO Fading Channels](https://mlsanity.com/paper/2406.07389/) (2024)
- [DM-MIMO: Diffusion Models for Robust Semantic Communications over MIMO Channels](https://mlsanity.com/paper/2407.05289/) (2024)
- [Semantic Edge Computing and Semantic Communications in 6G Networks: A Unifying Survey and Research Challenges](https://mlsanity.com/paper/2411.18199/) (2024)
- [Distortion Resilience for Goal-Oriented Semantic Communication](https://mlsanity.com/paper/2309.14587/) (2023)
- [Deep Joint Semantic Coding and Beamforming for Near-Space Airship-Borne Massive MIMO Network](https://mlsanity.com/paper/2405.19889/) (2024)
- [Generative Semantic HARQ: Latent-Space Text Retransmission and Combining](https://mlsanity.com/paper/2603.15068/) (2026)
- [TOAST: Task-Oriented Adaptive Semantic Transmission over Dynamic Wireless Environments](https://mlsanity.com/paper/2506.21900/) (2025)
- [Knowledge Abstraction for Knowledge-based Semantic Communication: A Generative Causality Invariant Approach](https://mlsanity.com/paper/2507.17784/) (2025)
- [Way to Build Native AI-driven 6G Air Interface: Principles, Roadmap, and Outlook](https://mlsanity.com/paper/2508.15277/) (2025)
- [Synesthesia of Machines (SoM)-Based Task-Driven MIMO System for Image Transmission](https://mlsanity.com/paper/2509.02031/) (2025)
- [Foundation Model-Based Adaptive Semantic Image Transmission for Dynamic Wireless Environments](https://mlsanity.com/paper/2509.23590/) (2025)
- [LLM4XCE: Large Language Models for Extremely Large-Scale Massive MIMO Channel Estimation](https://mlsanity.com/paper/2512.08955/) (2025)
- [A Survey on Semantic Communication for Vision: Categories, Frameworks, Enabling Techniques, and Applications](https://mlsanity.com/paper/2601.22202/) (2026)
- [Toward Polymorphic Backdoor against Semantic Communication via Intensity-Based Poisoning](https://mlsanity.com/paper/2604.23231/) (2026)

## Abstract (arXiv)

In existing semantic communication systems for image transmission, some images are generally reconstructed with considerably low quality. As a result, the reliable transmission of each image cannot be guaranteed, bringing significant uncertainty to semantic communication systems. To address this issue, we propose a novel performance metric to characterize the reliability of semantic communication systems termed semantic distortion outage probability (SDOP), which is defined as the probability of the instantaneous distortion larger than a given target threshold. Then, since the images with lower reconstruction quality are generally less robust and need to be allocated with more communication resources, we propose a novel framework of Semantic Communication with Adaptive chaNnel feedback (SCAN). It can reduce SDOP by adaptively adjusting the overhead of channel feedback for images with different reconstruction qualities, thereby enhancing transmission reliability. To realize SCAN, we first develop a deep learning-enabled semantic communication system for multiple-input multiple-output (MIMO) channels (DeepSC-MIMO) by leveraging the channel state information (CSI) and noise variance in the model design. We then develop a performance evaluator to predict the reconstruction quality of each image at the transmitter by distilling knowledge from DeepSC-MIMO. In this way, images with lower predicted reconstruction quality will be allocated with a longer CSI codeword to guarantee the reconstruction quality. We perform extensive experiments to demonstrate that the proposed scheme can significantly improve the reliability of image transmission while greatly reducing the feedback overhead.