Machine Learning-Based Cooperative Spectrum Sensing in Dynamic Segmentation Enabled Cognitive Radio Vehicular Network

Mohammad Asif Hossain, Rafidah Md Noor, Kok-Lim Alvin Yau, Saaidal Razalli Azzuhri, Muhammad Reza Z’aba, Ismail Ahmedy and Mohammad Reza Jabbarpour
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Mohammad Asif Hossain: Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur 50603, Malaysia
Rafidah Md Noor: Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur 50603, Malaysia
Kok-Lim Alvin Yau: School of Science and Technology, Sunway University, Selangor 47500, Malaysia
Saaidal Razalli Azzuhri: Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur 50603, Malaysia
Muhammad Reza Z’aba: Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur 50603, Malaysia
Ismail Ahmedy: Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur 50603, Malaysia
Mohammad Reza Jabbarpour: Department of Information and Communications Technology, Niroo Research Institute, Tehran 1468613113, Iran

Energies, 2021, vol. 14, issue 4, 1-30

Abstract: A vehicle ad hoc network (VANET) is a solution for road safety, congestion management, and infotainment services. Integration of cognitive radio (CR), known as CR-VANET, is needed to solve the spectrum scarcity problems of VANET. Several research efforts have addressed the concerns of CR-VANET. However, more reliable, robust, and faster spectrum sensing is still a challenge. A novel segment-based CR-VANET (Seg-CR-VANET) architecture is therefore proposed in this paper. Roads are divided equally into segments, and they are sub-segmented based on the probability value. Individual vehicles or secondary users produce local sensing results by choosing an optimal spectrum sensing (SS) technique using a hybrid machine learning algorithm that includes fuzzy and naïve Bayes algorithms. We used dynamic threshold values for the sensing techniques. In this proposed cooperative SS, the segment spectrum agent (SSA) made the global decision using the tri-agent reinforcement learning (TA-RL) algorithm. Three environments (network, signal, and vehicle) are learned by this proposed algorithm to determine primary (licensed) users’ activities. The simulation results indicate that, compared to current works, the proposed Seg-CR-VANET produces better results in spectrum sensing.

Keywords: spectrum sensing; cognitive radio; VANET; tri-agent reinforcement learning; machine learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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