Application of Machine Learning Algorithms Wireless Networks

Abstract

The rapid evolution of wireless networks has led to an increasing demand for intelligent and adaptive solutions to enhance network performance, security, and efficiency. Machine learning (ML) algorithms have emerged as a powerful tool in optimizing various aspects of wireless communication, including network management, spectrum allocation, interference mitigation, and security enhancement. This research paper explores the application of ML techniques in wireless networks, focusing on supervised, unsupervised, and reinforcement learning approaches. It examines how ML-based solutions improve network efficiency by enabling predictive maintenance, intelligent resource allocation, and adaptive modulation schemes. Additionally, the study highlights the role of ML in enhancing security through anomaly detection and intrusion prevention mechanisms. Challenges such as computational complexity, data privacy, and model interpretability are also discussed, along with potential future directions for integrating ML with next-generation 5G and 6G wireless networks. By leveraging ML-driven automation and intelligence, wireless networks can achieve greater reliability, adaptability, and resilience in dynamic environments.