arXiv cs.AI· Selim F. Yilmaz, Can Karamanli, Deniz Gunduz·· 13 小时前AI 评分18
基于多视角自编码器的非正交多址接入联合信源信道编码学习
Learning to Interfere in Non-Orthogonal Multiple-Access Joint Source-Channel Coding
AI 导读
研究提出一种ML辅助的无线图像传输方法，用多视角自编码器融合压缩与信道编码，让多个发射机同时占用全部信道资源形成NOMA方案，接收端从叠加信号中恢复全部图像并关联对应发射机。配套的渐进式微调算法每轮将用户数翻倍，在可训练参数仅比单用户模型增加2.4%的情况下支持最多64个用户。该方法在多种数据集、指标和信道条件下恢复图像质量显著优于现有NOMA方法。
正文 · 原文
Abstract:We consider multiple transmitters aiming to communicate their source signals (e.g., images) over a multiple access channel (MAC). Conventional communication systems minimize interference by orthogonally allocating resources (time and/or bandwidth) among users, which limits their capacity. We introduce a machine learning (ML)-aided wireless image transmission method that merges compression and channel coding using a multi-view autoencoder, which allows the transmitters to use all the available channel resources simultaneously, resulting in a non-orthogonal multiple access (NOMA) scheme. The receiver must recover all the images from the received superposed signal, while also associating each image with its transmitter. Traditional ML models deal with individual samples, whereas our model allows signals from different users to interfere in order to leverage gains from NOMA under limited bandwidth and power constraints. We introduce a progressive fine-tuning algorithm that doubles the number of users at each iteration, maintaining initial performance with orthogonalized user-specific projections, which is then improved through fine-tuning steps. Remarkably, our method supports up to 64 users with potential for further scaling, with only a 2.4% increase in the number of trainable parameters compared to a single-user model, significantly enhancing recovered image quality and outperforming existing NOMA-based methods over a wide range of datasets, metrics, and channel conditions. Our approach paves the way for more efficient and robust multi-user communication systems, leveraging innovative ML components and strategies.
| Comments: | 20 pages, 19 figures, 4 tables, including appendix. Published in IEEE Transactions on Wireless Communications |
| Subjects: | Networking and Internet Architecture (cs.NI); Artificial Intelligence (cs.AI); Information Theory (cs.IT); Machine Learning (cs.LG) |
| Cite as: | arXiv:2504.03690 [cs.NI] |
| (or arXiv:2504.03690v2 [cs.NI] for this version) | |
| https://doi.org/10.48550/arXiv.2504.03690 arXiv-issued DOI via DataCite |
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| Journal reference: | IEEE Transactions on Wireless Communications, vol. 25, pp. 16859-16873, 2026 |
| Related DOI: | https://doi.org/10.1109/TWC.2026.3688951
DOI(s) linking to related resources |
Submission history
From: Selim Firat Yilmaz [view email]
[v1]
Sun, 23 Mar 2025 12:27:20 UTC (9,379 KB)
[v2]
Wed, 21 Oct 2026 16:06:31 UTC (6,809 KB)
来源：arXiv cs.AI · arxiv.org