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ML-aided NOMA scheme boosts wireless image transmission for up to 64 users

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel machine learning approach for non-orthogonal multiple access (NOMA) wireless image transmission. This method utilizes a multi-view autoencoder to merge compression and channel coding, allowing multiple transmitters to share all channel resources simultaneously. A progressive fine-tuning algorithm enables the system to support up to 64 users while maintaining high recovered image quality, outperforming existing NOMA techniques. AI

IMPACT This ML-driven NOMA approach could significantly enhance the efficiency and scalability of multi-user wireless communication systems.

RANK_REASON Academic paper detailing a new ML-based communication technique. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

ML-aided NOMA scheme boosts wireless image transmission for up to 64 users

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Academic paper detailing a new ML-based communication technique. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Selim F. Yilmaz, Can Karamanli, Deniz Gunduz ·

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

    arXiv:2504.03690v2 Announce Type: replace-cross 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 resource…

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