Wireless Edge Caching and Content Popularity Prediction using Machine Learning

S. Krishnendu, B. N. Bharath, Vimal Bhatia, Jamel Nebhen, Michal Dobrovolny, Tharm Ratnarajah

Research output: Contribution to journalArticlepeer-review

Abstract

The ever pervasive growth in information services and technology has resulted in the outbreak of demand for data in the wireless networks. This has made the network operators to ponder over the imminent difficulties such as computing capabilities and fronthaul-backhaul link capacities. Hence, to bridge the gap between the cloud capacity and the requirement of mobile services by the network edges, edge computing and caching techniques have been gaining more and more attention from researchers across the world. Further, motivated by the successful applications of ML in solving complex and dynamic problems, in this article, it has been used to advance edge caching capabilities. The proposed ML based algorithms have been evaluated and proved to have better performance compared with the existing conventional algorithms. The MSE, for the proposed DL algorithm, is 20-times less than the existing algorithms and while comparing with the simple neural network, the gain in MSE for the proposed DL algorithm is observed around 27%. Similarly for the FL based caching algorithm, average cache hit gain is of the order of 10^4, hence demonstrating the benefit of the proposed algorithms. Additionally, opportunities for a promising upcoming future of ML in edge computing prediction have been discussed.
Original languageEnglish
Pages (from-to)1-9
Number of pages9
JournalIEEE Consumer Electronics Magazine
Early online date18 Mar 2022
DOIs
Publication statusE-pub ahead of print - 18 Mar 2022

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