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

• URL: http://arxiv.org/abs/2504.03690v1
• Date: Sun, 23 Mar 2025 12:27:20 GMT
• Title: Learning to Interfere in Non-Orthogonal Multiple-Access Joint Source-Channel Coding
• Authors: Selim F. Yilmaz, Can Karamanli, Deniz Gunduz,
• Abstract summary: We introduce a machine learning-aided wireless image transmission method that merges compression and channel coding using a multi-view autoencoder.<n>Our method scales up to 16 users and beyond, with only a 0.6% increase in the number of trainable parameters compared to a single-user model.
• Score: 0.25602836891933073
• License: http://creativecommons.org/licenses/by/4.0/
• 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 scales up to 16 users and beyond, with only a 0.6% 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.

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