English(EN) Learning to Interfere in Non-Orthogonal Multiple-Access Joint Source-Channel Coding

机器学习辅助的NOMA方案可为多达64个用户提升无线图像传输性能

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-22 04:00

研究人员开发了一种新颖的机器学习方法用于非正交多址接入（NOMA）无线图像传输。该方法利用多视图自编码器融合压缩和信道编码，允许多个发射机同时共享所有信道资源。一种渐进式微调算法使该系统能够支持多达64个用户，同时保持高恢复图像质量，性能优于现有的NOMA技术。 AI

影响这种由机器学习驱动的NOMA方法可以显著提高多用户无线通信系统的效率和可扩展性。

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arXiv cs.LG TIER_1 English(EN) · Selim F. Yilmaz, Can Karamanli, Deniz Gunduz · 2026-10-22 04:00

学习非正交多址联合信源信道编码中的干扰

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…