Deep Learning for Joint Narrowband Interference Cancellation and Soft Demodulation in OFDM Systems
2026-07-09 • Machine Learning
Machine Learning
AI summaryⓘ
The authors address the problem of narrowband interference (NBI) hurting OFDM communication systems, where traditional methods struggle to cleanly separate signal and noise and produce unreliable results. They propose a deep learning approach with two neural networks: one (NBI-CNet) to quickly estimate and cancel interference without knowing how many interferers are active, and another (LLR-CNet) to better interpret the cleaned signal for decoding. Their method reduces complexity, avoids error floors common in older methods, and performs close to optimal even under strong interference. Additionally, their design works well across different system sizes without needing retraining.
Orthogonal Frequency-Division Multiplexing (OFDM)Narrowband Interference (NBI)Compressed SensingSoft DemodulationLog-Likelihood Ratio (LLR)Deep LearningConvolutional Neural NetworksError FloorSignal-to-Interference Ratio (SIR)Fast Fourier Transform (FFT)
Authors
Emmanouil Kavvousanos, Francky Catthoor, Vassilis Paliouras
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) 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-CNet employs a physics-informed convolutional architecture to estimate NBI parameters and remove multi-tone interference in a single forward pass. Without requiring prior knowledge of the active interferer count, NBI-CNet reduces computational complexity by up to 60% ($N{=}2048, Q{=}64$) compared to the state-of-the-art EOMP-IDS algorithm. Second, LLR-CNet acts as a structural whitener by mapping non-Gaussian post-mitigation residuals onto well-calibrated soft metrics. Simulations demonstrate that this joint framework eliminates the error floors inherent to traditional baselines across dense grids. Under severe interference ($\text{SIR}{=}{-}10$ dB), the pipeline operates within a $0.2$ to $0.5$ dB SNR margin of the optimal iterative baseline at a target block error rate (BLER) of $10^{-4}$. Under mild interference ($\text{SIR}{=}10$ dB) with heavy spectral overlap ($Q{=}12$), where classical greedy algorithms erroneously subtract valid data components and corrupt the payload, NBI-CNet avoids signal-peak confusion to deliver a coding gain exceeding $3$ dB. Finally, the architecture circumvents the $2{\times}10^{-4}$ error floor triggered by interferer-estimation errors, while its scale-invariant design enables robust generalization across arbitrary FFT sizes without retraining.