Advanced Signal Decomposition and Noise Suppression Techniques for Robust Communication System Performance

Sneha Banerjee, Dr. Priya Sharma
Department of Electrical Engineering
Institute of Aeronautical Engineering, Hyderabad, India

Abstract

In today’s wireless and wired networks, the efficiency of communication systems is significantly dependent on proficient signal decomposition and noise suppression techniques. Communication channels are inherently influenced by various noise sources, such as thermal noise, multipath fading, co-channel interference, and non-stationary noise, which require advanced processing methods for accurate signal extraction. Traditional linear filtering and transform-based methods, like Fourier analysis, often fall short when dealing with non-stationary and nonlinear noise conditions. As a result, adaptive and hybrid techniques, including Empirical Mode Decomposition (EMD), Wavelet Transforms (WT), Variational Mode Decomposition (VMD), and machine learning-enhanced strategies, have emerged as effective solutions. This research article provides a comprehensive review, encompassing theoretical principles, methodologies, implementation specifics, comparative evaluations, and practical results of advanced signal decomposition and noise reduction techniques aimed at enhancing communication system performance. Notable contributions include a systematic categorization of decomposition methods, insights into implementation, case studies with performance metrics such as signal-to-noise ratio (SNR) and bit error rate (BER), and suggestions for future research. By integrating adaptive algorithms and computational intelligence, this article illustrates how modern decomposition techniques enhance robustness in diverse communication environments. These methods leverage the intrinsic properties of signals to adaptively isolate relevant components while reducing noise, thereby enhancing signal clarity. Machine learning techniques further augment these methods by enabling data-driven optimization and real-time adaptability in complex scenarios. The incorporation of these advanced decomposition and suppression strategies is crucial for meeting the increasing demands of high-speed, reliable communication systems.

Keywords

Signal decomposition techniques; Suppression of noise; Empirical Mode Decomposition; Wavelet Transform; Variational Mode Decomposition; Adaptive filtering; Communication systems; Signal resilience; Machine learning.