scholarly journals Eigenvalue Fusion based Machine Learning Approach for Cooperative Spectrum Sensing in Cognitive Radio

2022 ◽  
Vol 17 (3) ◽  
pp. 1
Author(s):  
Rajendra Yelalwar ◽  
Yerram Ravinder
Keyword(s):  
Machine Learning ◽  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cooperative Spectrum Sensing ◽  
Learning Approach ◽  
Machine Learning Approach

Machine Learning Approach in Cooperative Spectrum Sensing for Cognitive Radio Network: Survey

2021 ◽  
pp. 63-71
Author(s):  
Vaishali S. Kulkarni ◽  
Tanuja S. Dhope(Shendkar) ◽  
Swagat Karve ◽  
Pranav Chippalkatti ◽  
Akshay Jadhav
Keyword(s):  
Machine Learning ◽  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cognitive Radio Network ◽  
Cooperative Spectrum Sensing ◽  
Learning Approach ◽  
Radio Network ◽  
Machine Learning Approach

scholarly journals A Machine Learning Approach for Backoff Manipulation Attack Detection in Cognitive Radio

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 227349-227359
Author(s):  
Wassim Fassi Fihri ◽  
Hassan El Ghazi ◽  
Badr Abou El Majd ◽  
Faissal El Bouanani
Keyword(s):  
Machine Learning ◽  
Cognitive Radio ◽  
Attack Detection ◽  
Learning Approach ◽  
Machine Learning Approach

scholarly journals Exploiting Sparsity Recovery for Compressive Spectrum Sensing: A Machine Learning Approach

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 126098-126110 ◽  
Author(s):  
Mahmoud Nazzal ◽  
Ali Riza Ekti ◽  
Ali Gorcin ◽  
Huseyin Arslan
Keyword(s):  
Machine Learning ◽  
Spectrum Sensing ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Sparsity Recovery

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

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1169
Author(s):  
Mohammad Asif Hossain ◽  
Rafidah Md Noor ◽  
Kok-Lim Alvin Yau ◽  
Saaidal Razalli Azzuhri ◽  
Muhammad Reza Z’aba ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Ad Hoc ◽  
Cooperative Spectrum Sensing ◽  
Learning Algorithm ◽  
Congestion Management ◽  
Secondary Users ◽  
Dynamic Segmentation ◽  
Services Integration

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.

scholarly journals Cooperative Spectrum Sensing in Cognitive Radio Networks: A Survey on Machine Learning-based Methods

2020 ◽  
Vol 3 ◽  
pp. 36-46
Author(s):  
Sundous Khamayseh ◽  
Alaa Halawani
Keyword(s):  
Machine Learning ◽  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Cooperative Spectrum Sensing ◽  
Radio Networks ◽  
Continuous Growth ◽  
Radio Technology ◽  
Promising Solution

The continuous growth of demand experienced by wireless networks creates a spectrum availability challenge. Cognitive radio (CR) is a promising solution capable of overcoming spectrum scarcity. It is an intelligent radio technology that may be programmed and dynamically configured to avoid interference and congestion in cognitive radio networks (CRN). Spectrum sensing (SS) is a cognitive radio life cycle task aiming to detect spectrum holes. A number of innovative approaches are devised to monitor the spectrum and to determine when these holes are present. The purpose of this survey is to investigate some of these schemes which are constructed based on machine learning concepts and principles. In addition, this review aims to present a general classification of these machine learningbased schemes

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