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By: Jyoti Amit Kumar Dhamecha.
Assistant Professor, Sardar Patel College of Administration and Management, SPEC Campus, Bakrol-388315 6,” Divya Kunj” Sahajanand Colony, Sikhod Talavadi, Gujarat, India
Noise is one of the major challenges in modern communication systems because it significantly affects signal quality, transmission efficiency, and overall system reliability. Both digital and analog communication systems are influenced by different types of noise such as thermal noise, impulse noise, quantization noise, atmospheric noise, and channel interference. Conventional noise reduction techniques including filtering, adaptive equalization, and statistical signal processing have been extensively used to improve communication performance. However, the rapid growth of Artificial Intelligence (AI) has introduced advanced machine learning and deep learning techniques that provide more efficient and intelligent solutions for noise reduction. AI- based methods can more accurately and efficiently adjust to shifting communication settings by automatically learning intricate noise patterns. This research paper presents a detailed study of AI-based noise reduction techniques for digital and analog communication systems. The paper discusses the basic concepts of communication noise, traditional denoising approaches, and the implementation of AI algorithms such as Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Deep Learning (DL), and Reinforcement Learning (RL) for signal enhancement and noise suppression. The study also highlights the application of AI in wireless communication, speech and audio processing, image transmission, biomedical communication, and Internet of Things (IoT) systems.
Keywords – Artificial Intelligence, Noise Reduction, Digital Communication, Analog Communication, Machine Learning, Deep Learning, Signal Processing, Communication Systems.
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