Machine Learning-Based Noise Reduction in Analog Communication Signals A Comparative Study of Deep Learning Architectures for Signal Denoising

Abstract

Noise in analog communication channels significantly degrades signal quality, leading to increased bit error rates and reduced communication reliability. Traditional filtering approaches — including Wiener filters, Kalman filters, and wavelet-based methods — while effective under stationary conditions, fail to adapt to non-stationary, time-varying noise environments. This paper presents a comprehensive comparative study of three machine learning architectures — Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and a novel hybrid CNN-LSTM model — for noise reduction in analog communication signals across Additive White Gaussian Noise (AWGN), Rayleigh fading, and impulsive noise channels. We evaluate all models against classical baselines (Wiener filter, Empirical Mode Decomposition) using Signal-to-Noise Ratio improvement (ΔSNR), Mean Squared Error (MSE), and computational latency on a dataset of 50,000 synthetically generated and 10,000 real-world analog signal samples. Our proposed CNN-LSTM hybrid achieves a ΔSNR of 18.6 dB in AWGN channels, outperforming the next best baseline (Wiener filter) by 6.3 dB. In Rayleigh fading conditions, the model delivers an MSE of 3.2×10⁻⁴, representing a 52% reduction over classical methods. Real-time inference is demonstrated at 4.2 ms per sample on embedded hardware, confirming deployment feasibility.