Work

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

Year: 2025

Type: article

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 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%.

Source: EAI Endorsed Transactions on Internet of Things

DOI: 10.4108/eetiot.5910

Authors: Manikandan Parasuraman, Sivaram Rajeyyagari, Ramesh Sekaran, Suthendran Kannan, Vinayakumar Ravi

Institutions: Jain University, Shaqra University, Kalasalingam Academy of Research and Education, Prince Mohammad bin Fahd University

Language: English

Topic: IoT and Edge/Fog Computing

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: diamond

OpenAlex ID: W4407103933

FWCI: 0.437

Cites: 19

Cited by: 1

Related to: 10