Machine learning-based optimized link state routing protocol for D2D communication in 5G/B5G

Bunu, SM; Saraee, MH; Alani, OYK

Abstract

Device to Device (D2D) communication in Fifth
Generation (5G) and unavoidable in Beyond Fifth Generation
(B5G) technology is designed to increase network capacity by
offloading backhaul links and base stations traffic and
improving the performance of low signal nodes, thereby
providing fast and energy-efficient communication. D2D
communication has several challenges, out of which network
extension and data routing to out-of-coverage nodes is the area
of focus for this research. Optimized Link State Protocol version
2 (OLSRv2) is a popular Mobile Ad-hoc Network (MANET) and
Multi-hop D2D communication routing protocol. However, the
provision of the D2D-based OLSRv2 routing protocol presents
several issues including energy consumption, routing overhead,
and optimum relay selection. Therefore, this paper modifies the
OLSRv2 protocol by introducing Node’s Status (NS) according
to multiple criteria, namely node’s battery level, mobility speed,
node degree, and connection to a base station. The proposed
routing protocol employed the supervised machine learning
(ML) technique to aggregate the multiple criteria into a single
comprehensive metric to minimize routing overhead and energy
consumption caused by individually transmitting multiple
parameters. To determine the best ML techniques for the
proposed protocol, four supervised ML techniques, namely KNearest Neighbors (K-NN), Random Forest (RF) Multi-layer
Perception (MPL), Gradient Boosting Classifier (GBC) has been
used to train the model using training datasets generated from
the simulation of D2D network in NS-3. The simulation results
show that the RF model performed better than the other three
models as it consistently reported 100% accuracy and receiver
operating characteristic.

Citation

Bunu, S., Saraee, M., & Alani, O. (2022, September). Machine learning-based optimized link state routing protocol for D2D communication in 5G/B5G. Presented at The 4th International Conference on Electrical Engineering and Informatics (ICELTICs) 2022, Banda Aceh, Indonesia

• Presentation Conference Type: Other
• Conference Name: The 4th International Conference on Electrical Engineering and Informatics (ICELTICs) 2022
• Conference Location: Banda Aceh, Indonesia
• Start Date: Sep 27, 2022
• End Date: Sep 28, 2022
• Acceptance Date: Aug 29, 2022
• Publication Date: Sep 27, 2022
• Deposit Date: Oct 14, 2022
• Publisher URL: https://iceltics.unsyiah.ac.id/
• Related Public URLs: https://iceltics.unsyiah.ac.id/
• Additional Information: Event Type : Conference