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 structured, non-Gaussian residuals that cause log-likelihood ratio (LLR) unreliability, decoder saturation, and severe error floors when employing classical Gaussian demappers. We resolve this pipeline mismatch using a unified deep learning framework for joint NBI cancellation and robust soft demodulation. First, NBI-CN
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- LinkedLinked via arxiv author · 85%Emmanouil Kavvousanos →
“Deep Learning for Joint Narrowband Interference Cancellation and Soft Demodulation in OFDM Systems”
- LinkedLinked via arxiv author · 85%Francky Catthoor →
“Deep Learning for Joint Narrowband Interference Cancellation and Soft Demodulation in OFDM Systems”
- LinkedLinked via arxiv author · 85%Vassilis Paliouras →
“Deep Learning for Joint Narrowband Interference Cancellation and Soft Demodulation in OFDM Systems”
- FuzzySimilar title/name (fuzzy) · 87%aymericdamien/TopDeepLearning →
“Fuzzy title match (0.94): “Deep Learning for Joint Narrowband Interference Cancellation” ≈ “aymericdamien/TopDeepLearning””