English(EN) Deep Learning for Joint Narrowband Interference Cancellation and Soft Demodulation in OFDM Systems

深度学习框架解决了OFDM系统中的干扰问题

作者 PulseAugur 编辑部 · [2 个来源] · 2026-07-09 17:26

研究人员开发了一种新颖的深度学习框架，用于解决正交频分复用（OFDM）系统中的窄带干扰（NBI）。该框架将NBI抑制和软解调整合到单一流程中，显著降低了计算复杂度并提高了可靠性。该方法旨在克服传统方法的局限性，传统方法通常会留下残余干扰并导致数据解码错误。 AI

影响这项研究通过改善在复杂干扰环境下的信号质量，可能带来更强大、更高效的通信系统。

排序理由该集群包含一篇详细介绍用于信号处理的新深度学习框架的学术论文。

AI 生成摘要 · Google Gemini · 来自 2 个来源。我们如何撰写摘要 →

报道来源 [2]

arXiv cs.LG TIER_1 English(EN) · Emmanouil Kavvousanos, Francky Catthoor, Vassilis Paliouras · 2026-07-10 04:00

Deep Learning for Joint Narrowband Interference Cancellation and Soft Demodulation in OFDM Systems

arXiv:2607.08717v1 Announce Type: new Abstract: Narrowband interference (NBI) severely degrades orthogonal frequency-division multiplexing (OFDM) systems by corrupting subcarriers and rendering classical soft demodulation ineffective. Conventional compressed-sensing (CS) mitigati…
arXiv cs.LG TIER_1 English(EN) · Vassilis Paliouras · 2026-07-09 17:26

Deep Learning for Joint Narrowband Interference Cancellation and Soft Demodulation in OFDM Systems

Narrowband interference (NBI) severely degrades orthogonal frequency-division multiplexing (OFDM) systems by corrupting subcarriers and rendering classical soft demodulation ineffective. Conventional compressed-sensing (CS) mitigation exhibits high sequential latency and leaves s…