AI-Powered Optimization of 5G Network Performance for Urban and Rural Deployment

AI-Powered Optimization of 5G Network Performance for Urban and Rural Deployment

Prof. Pritam Deshmukh mam, Shweta Patil

Associate Professor, Department of Electrical, Marathwada Mitra Mandal College of Engineering, Pune, India,

Student, Department of Electrical, Marathwada Mitra Mandal College of Engineering, Pune, India,

Abstract: The widespread deployment of 5G networks introduces considerable challenges in optimizing performance, coverage, and energy efficiency, particularly across urban and rural environments. Urban networks often face traffic congestion and high latency, whereas rural areas struggle with inadequate coverage and limited infrastructure. Traditional network management techniques are insufficient for adapting to real-time traffic conditions, resulting in suboptimal resource allocation.

This paper explores the integration of Artificial Intelligence (AI) and Machine Learning (ML) for enhancing 5G performance in diverse environments. We propose AI-based Reinforcement Learning (RL) and Deep Learning (DL) approaches for intelligent resource allocation and real-time traffic forecasting.

In urban scenarios, AI-enabled load balancing mitigates congestion and reduces latency. In rural deployments, AI-driven beamforming and smart antenna systems enhance signal quality and expand coverage.

Our findings suggest that AI-based adaptive network optimization significantly improves the efficiency, reliability, and scalability of 5G networks. Future research can explore AI-enhanced cybersecurity and optimization strategies for 6G networks.

Keywords: 5G Optimization, AI, Machine Learning, Network Resource Management, Latency Reduction, Deep Learning, Network Slicing