用深度学习联合消除窄带干扰并可靠软解调，解决传统方法误判难题。
Deep Learning for Joint Narrowband Interference Cancellation and Soft Demodulation in OFDM Systems
- 设计物理引导的卷积网络，单次前向传播完成多频干扰估计与清除
- 在-10 dB干扰下仅需0.2~0.5 dB SNR余量即可达10^-4块错误率
- 可避免信号峰混淆，对任意傅里叶变换尺寸无需重训练
窄带干扰（NBI）严重损害正交频分复用（OFDM）系统，破坏子载波并使经典软解调失效。传统压缩感知（CS）方法存在高串行延迟，且残留结构化、非高斯特征导致对数似然比（LLR）不可靠、译码器饱和及严重误码地板。本文提出统一深度学习框架，实现联合NBI消除与鲁棒软解调，解决这一流程不匹配问题。首先，NBI-CNet采用物理引导的卷积架构，单次前向传播即可估计干扰参数并去除多音干扰，无需预先知晓干扰源数量，在 $N{=}2048, Q{=}64$ 条件下计算复杂度降低最高达60%，优于最先进算法EOMP-IDS。其次，LLR-CNet作为结构白化器，将后处理残留映射为校准良好的软度量。仿真表明，该联合框架在密集频谱网格中彻底消除传统基线的误码地板。在严重干扰（SIR = -10 dB）下，系统在目标块错误率（BLER）为 $10^{-4}$ 时，仅需0.2至0.5 dB SNR余量即接近最优迭代基线。在轻度干扰（SIR = 10 dB）且频谱重叠严重（Q = 12）时，传统贪心算法会错误扣除有效数据成分，造成信道污染；而NBI-CNet有效避免信号峰混淆，实现超过3 dB编码增益。最终，该架构克服了由干扰估计误差引发的 $2\times10^{-4}$ 误码地板，并凭借尺度不变设计，在任意FFT大小下实现无需重训的稳健泛化。
原文摘要 · Abstract (English)
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.
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