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

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.