AI 中文总结
研究通信信号小波去噪，综述2020 - 2025年相关研究，发现从固定经验选择到数据驱动选择的转变及验证不足。通过多种指标对小波变换进行OFDM去噪基准测试，开发统一决策框架并验证，其性能显著优于基线。
AI 中文摘要
小波去噪可抑制通信信号中的非平稳、脉冲和类似干扰的干扰，但其有效性取决于联合选择变换族、母小波、分解级别、阈值规则和收缩函数。本文综述了2020 - 2025年发表在十个来源的研究，按参数选择重点和应用领域进行分类。结果表明从固定经验选择向基于相似性、稀疏性、熵、能量、子带SNR和任务损失驱动的选择转变，且通信特定验证有限。通过多种指标对DWT、SWT和WPT进行OFDM去噪基准测试，结果显示波形保真度提高不一定转化为更好的硬判决性能。因此开发了统一决策框架并在合成导频辅助OFDM信道估计和实测IEEE 802.11n信道上进行验证，所选配置显著优于固定参数小波和经典基线，具有最低估计误差等优点。
英文摘要
Wavelet denoising suppresses nonstationary, impulsive, and interference-like disturbances in communication signals, but its effectiveness depends on jointly selecting the transform family, mother wavelet, decomposition level, thresholding rule, and shrinkage function. This review synthesises studies published during 2020--2025 across ten sources using a PRISMA-aligned protocol and classifies them by parameter-selection focus and application domain. The evidence shows a shift from fixed empirical choices toward similarity-, sparsity-, entropy-, energy-, sub-band-SNR-, and task-loss-driven selection, while revealing limited communication-specific validation. To address this gap, DWT, SWT, and WPT are benchmarked for OFDM denoising under impulsive noise using SNR gain, MSE, BER, EVM, real-time feasibility, Friedman and Wilcoxon tests, efficiency-index ranking, and embedded DSP/FPGA constraints. Results show that improved waveform fidelity does not necessarily translate into better hard-decision performance, motivating receiver-level validation. A Unified Decision Framework is therefore developed and validated on synthetic pilot-aided OFDM channel estimation and measured IEEE 802.11n channels using BER, EVM, NMSE, and SNR gain. The selected configuration significantly outperforms fixed-parameter wavelet and classical baselines $\left(p < 10^{-11}\right)$, achieves the lowest estimation error, generalises to held-out data, adapts to channel conditions, and supports extension to deep-unfolding architectures.
Comments42 pages. This version is under peer review
