Wireless 5G Network in Edge Computing Based On MIMO with Federated Learning and Clustering Integrated Reinforcement Learning

Edge Computing (EC) is a revolutionary architecture that brings Cloud Computing (CC) services closer to data sources than ever before. This research proposed novel technique in edge computing network based on wireless 5G technology using MIMO_federated learning integrated with Reinforcement neural network. Here the aim is to enhance the resource allocation by Decentralized Federated learning in multiple user based MIMO (De_Fed_L- MIMO) networks. Then the energy efficiency and channel optimization of the network is carried out using K-means clustering integrated with Reinforcement learning (K-means_RL). Here the experimental analysis is carried out in terms of number of users of network as well as number of edge server by DoF of 92%, Spectral efficiency of 92%, Energy efficiency of 96%, Signal to noise ratio (SNR) of 85%, Coverage area of 92%, RL training accuracy of 95%, FL training accuracy of 98%.