Research Article
Wireless 5G Network in Edge Computing Based On MIMO with Federated Learning and Clustering Integrated Reinforcement Learning
@ARTICLE{10.4108/eetiot.5910, author={Manikandan Parasuraman and Sivaram Rajeyyagari and Ramesh Sekaran and Suthendran Kannan and Vinayakumar Ravi}, title={Wireless 5G Network in Edge Computing Based On MIMO with Federated Learning and Clustering Integrated Reinforcement Learning}, journal={EAI Endorsed Transactions on Internet of Things}, volume={11}, number={1}, publisher={EAI}, journal_a={IOT}, year={2025}, month={4}, keywords={Edge Computing, MIMO, 5G cellular networks, federated learning, resource allocation, energy efficiency and channel optimization}, doi={10.4108/eetiot.5910} }- Manikandan Parasuraman
Sivaram Rajeyyagari
Ramesh Sekaran
Suthendran Kannan
Vinayakumar Ravi
Year: 2025
Wireless 5G Network in Edge Computing Based On MIMO with Federated Learning and Clustering Integrated Reinforcement Learning
IOT
EAI
DOI: 10.4108/eetiot.5910
Abstract
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 MIMOfederated learning integrated with Reinforcement neural network. Here the aim is to enhance the resource allocation by Decentralized Federated learning in multiple user based MIMO (DeFedL- MIMO) networks. Then the energy efficiency and channel optimization of the network is carried out using K-means clustering integrated with Reinforcement learning (K-meansRL). 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%.
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