A Machine Learning Approach to Enhance the Performance of D2D-Enabled Clustered Networks

Abstract

Clustering has been suggested as an effective technique to enhance the performance of multicasting networks. Typically, a cluster head is selected to broadcast the cached content to its cluster members utilizing Device-to-Device (D2D) communication. However, some users can attain better performance by being connected with the Evolved Node B (eNB) rather than being in the clusters. In this article, we apply machine learning algorithms, namely Support Vector Machine, Random Forest, and Deep Neural Network to identify the users that should be serviced by the eNB. We therefore propose a mixed-mode content distribution scheme where the cluster heads and eNB service the two segregated groups of users to improve the performance of existing clustering schemes. A D2D-enabled multicasting scenario has been set up to perform a comprehensive simulation study that demonstrates that by utilizing the mixed-mode scheme, the performance of individual users, as well as the whole network, improve significantly in terms of throughput, energy consumption, and fairness. This study also demonstrates the trade-off between eNB loading and performance improvement for various parameters. INDEX TERMSClustering algorithm, content multicasting, D2D enabled networks, deep neural networks, eNB loading, machine learning, random forest, support vector machine, user segregation.

Models, Methods, and Solutions for Multicasting in 5G/6G mmWave and Sub-THz Systems

“…A strategy based on unsupervised ML is exploited for performing a dynamic division of D2D users into groups, while an algorithm that embeds Q-Learning is used to maximize the energy efficiency of involved UEs. The work in [156] proposes a mixed-mode content distribution scheme for D2D-enabled clustered networks wherein the users that should be serviced by the eNB are determined by means of ML.…”

Cluster formation in device-to-device (D2D) communications to ensure quality of service & minimize cluster head power consumptions

“…According to [10], cluster formation algorithms proposed for D2D communications can be grouped as: squared error-based algorithm, similarity-based, hierarchical based algorithms, density-based clustering algorithms, etc. Recently, machine learning algorithms have been applied in D2D communication and in particular to cluster D2D UEs [10,11]. In addition to the choice of algorithm, various cluster data input and criteria had been adopted in literatures to cluster the UEs and select the appropriate CH.…”

“…Machine learning algorithms such as SOM need data to perform cluster formation. Some of these data can be collected by the UEs themselves, by the Radio Access Network (RAN) or by the core network [10]. The data used in this study were collected by the UEs.…”

A Survey on Machine Learning Techniques for Massive MIMO Configurations: Application Areas, Performance Limitations and Future Challenges

“…These approaches can be applied both in SL as well as in UL modes. The most important clustering methods include support vector machines (SVMs) [41], k-nearest neighbors (k-NN) clustering [42], decision trees, etc. For example, in k-NN a commonly used distance metric for continuous variables is the Euclidean distance.…”