这篇论文用一个统一深度学习框架同时搞定OFDM窄带干扰消除和软解调，计算量比现有方法低60%，还能根治错误平层，强干扰下编码增益超3dB，做通信系统研究值得一看。
窄带干扰（NBI）会严重破坏OFDM系统的子载波，传统压缩感知方法存在高顺序延迟和非高斯残差。新框架NBI-CNet采用物理信息卷积架构，一步完成多音干扰估计与消除，计算复杂度相比EOMP-IDS算法降低60%（N=2048, Q=64）。LLR-CNet将非高斯后处理残差映射为校准的软度量，消除传统基线在密集栅格中的错误平层。在SIR=-10dB的严重干扰下，目标BLER=10^-4时，框架的SNR裕度仅比最优迭代基线高0.2-0.5dB；在SIR=10dB、Q=12的轻度干扰下，编码增益超过3dB。该架构还能规避由干扰估计误差触发的2×10^-4错误平层，且无需重训练即可泛化到任意FFT尺寸。
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-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.