Optimized Coordinated Resource Allocation and Power Control for Wireless Communication Networks

This paper investigates an optimized approach to coordinated resource allocation and power control for wireless communication networks. To address limitations in existing methods, such as scalability and fairness, a novel algorithm is implemented that leverages federated learning regression-based decision trees to achieve efficient resource allocation and improved network performance. The system model considers a dense communication network with different user equipment types and incorporates constraints like power limitations and quality of service requirements. Simulation results demonstrate that the implemented approach significantly outperforms existing techniques in terms of throughput, energy efficiency, and latency.