Advances in Wavelet Denoising for Communication Signals: From Parameter Selection Toward Data-Driven Optimization

arXiv:2607.21825v2 Announce Type: replace Abstract: Wavelet denoising suppresses nonstationary, impulsive, and interference-like disturbances in communication signals, but efficacy depends on coupled choices of transform, mother wavelet, decomposition level, thresholding rule, and shrinkage function. Pairing communication experiments with a PRISMA-aligned synthesis of 2020-2025 studies from ten sources, coded by parameter focus and domain (biomedical, industrial, geophysical, communication), this review traces a shift from defaults to similarity-, sparsity-, entropy-, energy-, sub-band-SNR-, and task-loss-driven selection, but finds communication evidence scarce. We benchmark DWT, SWT, and WPT on an impulsive-noise OFDM channel under SNR gain, MSE, BER, EVM, and real-time feasibility, with Friedman and Wilcoxon tests, efficiency-index ranking, and assessment of embedded DSP/FPGA constraints. As waveform-fidelity gains need not improve hard-decision performance, the Unified Decision Framework prioritises receiver-level validation over fidelity surrogates, validated on synthetic pilot-aided OFDM and measured IEEE 802.11n channels. On the synthetic link, the configuration attains the lowest channel-estimation NMSE, significantly outperforms a fixed-parameter wavelet $(p