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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1. Singasani TR, Shaik MS, Gangajaliya C, Ogety SS, Nallam VA, Katta SK. AI-driven signal processing: Improving communication systems with machine learning-based noise reduction. In2025 1st International Conference on Radio Frequency Communication and Networks (RFCoN) 2025 Jun 19 (pp. 1-6). IEEE.
2. Kim P. Deep learning. InMATLAB deep learning: with machine learning, neural networks and artificial intelligence 2017 Jun 16 (pp. 103-120). Berkeley, CA: Apress.
3. Feng D, Jiang C, Lim G, Cimini LJ, Feng G, Li GY. A survey of energy-efficient wireless communications. IEEE Communications Surveys & Tutorials. 2012 Feb 24;15(1):167-78.
4. Galappaththige D, Tellambura C, Herath S. Wideband Cognitive Radio for Joint Communication and Sensing: Optimization of Subcarrier Allocation and Beamforming. IEEE Transactions on Cognitive Communications and Networking. 2025 Sep 2.
5. Li M, Guan Y, Yu B, Hu S. Research on Noise Reduction Technology of Digital Conference Power Amplifiers Based on AI Algorithms. In2025 IEEE 3rd International Conference on Sensors, Electronics and Computer Engineering (ICSECE) 2025 Aug 29 (pp. 646-650). IEEE.
6. RJS JK, Andrushia AD, Sathishkumar VE. Frontiers in digital and analog signal processing circuits and systems in the era of artificial intelligence (AI). InSignal Processing Roadmap 2026 Jan 1 (pp. 19-28). Morgan Kaufmann.
7. Mariappan US, Rajasekar RK, Saravanan I, Batcha MS. Applications of ML and AI based technologies for real-time signal processing on autonomous system. InAIP Conference Proceedings 2023 Aug 24 (Vol. 2790, No. 1, p. 020009). AIP Publishing LLC.
8. Milovančević M. AI-Driven Signal Processing and Network Management for Next-Generation Communications. Journal of AI-Driven Communication Engineering. 2025 Dec 30;1:59-71.
9. Moubarak Z, Monteiro F, Aarif LH, Dandache A, Tabaa M. AI Filtering System for Optimizing Wavelet-Based Industrial Communications. InJournal of Physics: Conference Series 2025 Jun 1 (Vol. 3028, No. 1, p. 012042). IOP Publishing.
10. Yang H. Implementation of AI techniques in digital wireless communications. InIET Conference Proceedings CP901 2024 Nov 22 (Vol. 2024, No. 24, pp. 376-381). Stevenage, UK: The Institution of Engineering and Technology.

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.

🔒 This is a subscription article

Full text is available to subscribers and institutional members. Please choose an option below to access it.

1. Singasani TR, Shaik MS, Gangajaliya C, Ogety SS, Nallam VA, Katta SK. AI-driven signal processing: Improving communication systems with machine learning-based noise reduction. In2025 1st International Conference on Radio Frequency Communication and Networks (RFCoN) 2025 Jun 19 (pp. 1-6). IEEE.
2. Kim P. Deep learning. InMATLAB deep learning: with machine learning, neural networks and artificial intelligence 2017 Jun 16 (pp. 103-120). Berkeley, CA: Apress.
3. Feng D, Jiang C, Lim G, Cimini LJ, Feng G, Li GY. A survey of energy-efficient wireless communications. IEEE Communications Surveys & Tutorials. 2012 Feb 24;15(1):167-78.
4. Galappaththige D, Tellambura C, Herath S. Wideband Cognitive Radio for Joint Communication and Sensing: Optimization of Subcarrier Allocation and Beamforming. IEEE Transactions on Cognitive Communications and Networking. 2025 Sep 2.
5. Li M, Guan Y, Yu B, Hu S. Research on Noise Reduction Technology of Digital Conference Power Amplifiers Based on AI Algorithms. In2025 IEEE 3rd International Conference on Sensors, Electronics and Computer Engineering (ICSECE) 2025 Aug 29 (pp. 646-650). IEEE.
6. RJS JK, Andrushia AD, Sathishkumar VE. Frontiers in digital and analog signal processing circuits and systems in the era of artificial intelligence (AI). InSignal Processing Roadmap 2026 Jan 1 (pp. 19-28). Morgan Kaufmann.
7. Mariappan US, Rajasekar RK, Saravanan I, Batcha MS. Applications of ML and AI based technologies for real-time signal processing on autonomous system. InAIP Conference Proceedings 2023 Aug 24 (Vol. 2790, No. 1, p. 020009). AIP Publishing LLC.
8. Milovančević M. AI-Driven Signal Processing and Network Management for Next-Generation Communications. Journal of AI-Driven Communication Engineering. 2025 Dec 30;1:59-71.
9. Moubarak Z, Monteiro F, Aarif LH, Dandache A, Tabaa M. AI Filtering System for Optimizing Wavelet-Based Industrial Communications. InJournal of Physics: Conference Series 2025 Jun 1 (Vol. 3028, No. 1, p. 012042). IOP Publishing.
10. Yang H. Implementation of AI techniques in digital wireless communications. InIET Conference Proceedings CP901 2024 Nov 22 (Vol. 2024, No. 24, pp. 376-381). Stevenage, UK: The Institution of Engineering and Technology.