Efficient algorithm for resource optimization in optical communication networks

Beyond 5 G and 6 G, communication systems should be able to deliver high throughput, low latency, high dependability, and high energy efficiency services. The creation of hybrid systems that can meet and largely satisfy these needs is promised by the merging of systems based on optical communication and radio frequency (RF). Smart devices may work together to cooperatively train Machine Learning (ML) models in a distributed fashion using Federated Learning (FL), all without disclosing personal information to a central server. This paper proposes a new solution to optimize the network resources in optical-RF communication network. The main idea is to optimize user selection, transmission power and channel estimation based on multilayer perception. Then, the loss function is minimized through joint optimization of user selection and transmission power. Simulation results show that, the proposed algorithm has better performance as compared with existing algorithms.