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arXiv Machine Learning By Selim F. Yilmaz, Can Karamanli, Deniz Gunduz

Learning to Interfere in Non-Orthogonal Multiple-Access Joint Source-Channel Coding

Read the original on arXiv Machine Learning →

The paper proposes a machine‑learning–based wireless image transmission scheme that merges compression and channel coding via a multi‑view autoencoder, enabling multiple transmitters to share the same channel resources simultaneously in a non‑orthogonal multiple‑access (NOMA) setting. A progressive fine‑tuning algorithm doubles the number of users at each iteration while preserving orthogonalized performance, allowing the system to support up to 64 users with only a 2.4 % increase in trainable parameters. The method improves recovered image quality and outperforms existing NOMA‑based approaches across various datasets, metrics, and channel conditions.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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