# DeepMA: End-to-end Deep Multiple Access for Wireless Image Transmission in Semantic Communication

arXiv: https://arxiv.org/abs/2303.11543 · HTML: https://mlsanity.com/paper/2303.11543/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Mar 2023.

## Built on by (most cited first, 12 of 12)

- [Agent-driven Generative Semantic Communication with Cross-Modality and Prediction](https://mlsanity.com/paper/2404.06997/) (2024)
- [A Semantic-Aware Multiple Access Scheme for Distributed, Dynamic 6G-Based Applications](https://mlsanity.com/paper/2401.06308/) (2024)
- [Semantic Edge-Cloud Communication for Real-Time Urban Traffic Surveillance with ViT and LLMs over Mobile Networks](https://mlsanity.com/paper/2509.21259/) (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)
- [Adapting Diffusion Language Models for Lossless Pixel-Level Image Transmission](https://mlsanity.com/paper/2606.06273/) (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)
- [A Survey on Semantic Communication for Vision: Categories, Frameworks, Enabling Techniques, and Applications](https://mlsanity.com/paper/2601.22202/) (2026)
- [Contextual Memory-Enhanced Source Coding for Low-SNR Communications](https://mlsanity.com/paper/2605.04400/) (2026)
- [Wireless Backdoor Attack and Defense for Semantic Communications over Multiple Access Channel](https://mlsanity.com/paper/2606.30595/) (2026)

## Abstract (arXiv)

Semantic communication is a new paradigm that exploits deep learning models to enable end-to-end communications processes, and recent studies have shown that it can achieve better noise resiliency compared with traditional communication schemes in a low signal-to-noise (SNR) regime. To achieve multiple access in semantic communication, we propose a deep learning-based multiple access (DeepMA) method by training semantic communication models with the abilities of joint source-channel coding (JSCC) and orthogonal signal modulation. DeepMA is achieved by a DeepMA network (DMANet), which is comprised of several independent encoder-decoder pairs (EDPs), and the DeepMA encoders can encode the input data as mutually orthogonal semantic symbol vectors (SSVs) such that the DeepMA decoders can detect and recover their own target data from a received mixed SSV (MSSV) superposed by multiple SSV components transmitted from different encoders. We describe frameworks of DeepMA in wireless device-to-device (D2D), downlink, and uplink channel multiplexing scenarios, along with the training algorithm. We evaluate the performance of the proposed DeepMA in wireless image transmission tasks and compare its performance with the attention module-based deep JSCC (ADJSCC) method and conventional communication schemes using better portable graphics (BPG) and Low-density parity-check code (LDPC). The results obtained show that the proposed DeepMA can achieve effective, flexible, and privacy-preserving channel multiplexing process, and demonstrate that our proposed DeepMA approach can yield comparable bandwidth efficiency compared with conventional multiple access schemes.