# Semantic Communication with Memory

arXiv: https://arxiv.org/abs/2303.12335 · HTML: https://mlsanity.com/paper/2303.12335/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Mar 2023.

## Built on by (most cited first, 13 of 13)

- [Applications of Generative AI (GAI) for Mobile and Wireless Networking: A Survey](https://mlsanity.com/paper/2405.20024/) (2024)
- [BC4LLM: Trusted Artificial Intelligence When Blockchain Meets Large Language Models](https://mlsanity.com/paper/2310.06278/) (2023)
- [Task-Oriented Semantic Communication in Large Multimodal Models-based Vehicle Networks](https://mlsanity.com/paper/2505.02413/) (2025)
- [Distortion Resilience for Goal-Oriented Semantic Communication](https://mlsanity.com/paper/2309.14587/) (2023)
- [Latency-Distortion Tradeoffs in Communicating Classification Results over Noisy Channels](https://mlsanity.com/paper/2404.14586/) (2024)
- [Large Language Model-Driven Distributed Integrated Multimodal Sensing and Semantic Communications](https://mlsanity.com/paper/2505.18194/) (2025)
- [SC-GIR: Goal-oriented Semantic Communication via Invariant Representation Learning](https://mlsanity.com/paper/2509.01119/) (2025)
- [In-Context Source and Channel Coding](https://mlsanity.com/paper/2601.10267/) (2026)
- [Knowledge Abstraction for Knowledge-based Semantic Communication: A Generative Causality Invariant Approach](https://mlsanity.com/paper/2507.17784/) (2025)
- [Semantic Communication with Hopfield Memories](https://mlsanity.com/paper/2511.10302/) (2025)
- [Communication-Efficient Multi-Modal Edge Inference via Uncertainty-Aware Distributed Learning](https://mlsanity.com/paper/2601.14942/) (2026)
- [Evolving Token Communication with Parametric Memory Network](https://mlsanity.com/paper/2605.01869/) (2026)
- [Contextual Memory-Enhanced Source Coding for Low-SNR Communications](https://mlsanity.com/paper/2605.04400/) (2026)

## Abstract (arXiv)

While semantic communication succeeds in efficiently transmitting due to the strong capability to extract the essential semantic information, it is still far from the intelligent or human-like communications. In this paper, we introduce an essential component, memory, into semantic communications to mimic human communications. Particularly, we investigate a deep learning (DL) based semantic communication system with memory, named Mem-DeepSC, by considering the scenario question answer task. We exploit the universal Transformer based transceiver to extract the semantic information and introduce the memory module to process the context information. Moreover, we derive the relationship between the length of semantic signal and the channel noise to validate the possibility of dynamic transmission. Specially, we propose two dynamic transmission methods to enhance the transmission reliability as well as to reduce the communication overhead by masking some unessential elements, which are recognized through training the model with mutual information. Numerical results show that the proposed Mem-DeepSC is superior to benchmarks in terms of answer accuracy and transmission efficiency, i.e., number of transmitted symbols.