AI-Driven Network Optimization in 5G Deployment

Raguvaran S., Vishnu Kumar Kaliappan, Sam Goundar, Jueying Li, Maruliya Begam Kadarmydeen

Abstract


The rollout of 5G networks introduces unprecedented challenges in network management and optimization due to its increased complexity, higher data speeds, and ultra-low latency requirements. This paper investigates the use of AI-driven solutions to optimize 5G network deployment. AI techniques such as machine learning and neural networks can automate tasks such as load balancing, resource allocation, and fault detection, leading to improved performance and efficiency. Case studies demonstrate how AI-based systems can dynamically adjust network parameters to ensure optimal coverage, bandwidth utilization, and quality of service. This approach results in enhanced user experience and cost-efficient network operations.

Keywords


AI, 5G, network optimization, machine learning

References


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