Citation:

Vikram Nattamai Sankaran, Dr. M. Sivasankari, Rakesh Thoppaen Suresh Babu, 2023. Machine Learning in Wireless Networks: Optimizing Data Flow and Connectivity, ESP Journal of Engineering & Technology Advancements  3(3): 64-77.

Abstract:

In recent years, the proliferation of wireless networks and the increasing demand for efficient data transmission have underscored the need for advanced optimization techniques. Machine learning (ML) has emerged as a transformative tool in enhancing the performance and reliability of wireless networks. This paper explores the application of machine learning algorithms to optimize data flow and connectivity in wireless networks. We examine various ML techniques, including supervised learning, reinforcement learning, and unsupervised learning, and their effectiveness in addressing challenges such as traffic management, network congestion, and resource allocation. Through a comprehensive review of recent studies and experimental results, we highlight key ML-driven strategies for improving network efficiency, enhancing Quality of Service (QoS), and adapting to dynamic network conditions. Our findings suggest that integrating ML into network management can lead to significant improvements in data throughput and connectivity stability. The paper concludes with a discussion on future research directions and potential challenges in deploying ML solutions at scale.

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Keywords:

Machine Learning, Wireless Network, Data Flow Optimization, Connectivity Enhancement, Network Management, Supervised Learning, Unsupervised Learning, Reinforcement Learning, Network Performance Metrics.