scholarly journals A Strong Machine Learning Classifier and Decision Stumps Based Hybrid AdaBoost Classification Algorithm for Cognitive Radios

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5077 ◽  
Author(s):  
Siji Chen ◽  
Bin Shen ◽  
Xin Wang ◽  
Sang-Jo Yoo
Keyword(s):  
Machine Learning ◽  
Spectrum Sensing ◽  
Cooperative Spectrum Sensing ◽  
Cognitive Radios ◽  
Radio Networks ◽  
Adaptive Boosting ◽  
Adaboost Algorithm ◽  
Second Stage ◽  
Learning Classifier

Machine learning (ML) based classification methods have been viewed as one kind of alternative solution for cooperative spectrum sensing (CSS) in recent years. In this paper, ML techniques based CSS algorithms are investigated for cognitive radio networks (CRN). Specifically, a strong machine learning classifier (MLC) and decision stumps (DS) based adaptive boosting (AdaBoost) classification mechanism is proposed for pattern classification of the primary user’s behavior in the network. The conventional AdaBoost algorithm only combines multiple sub-classifiers and produces a strong weight based on their weights in classification. Taking into account the fact that the strong MLC and the weak DS serve as different sub-classifiers in classification, we propose employing a strong MLC as the first-stage classifier and DS as the second-stage classifiers, to eventually determine the class that the spectrum energy vector belongs to. We verify in simulations that the proposed hybrid AdaBoost algorithms are capable of achieving a higher detection probability than the conventional ML based spectrum sensing algorithms and the conventional hard fusion based CSS schemes.

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

A Reliable Cooperative Spectrum Sensing Strategy for Cognitive Radios

2013 ◽  
Vol 347-350 ◽  
pp. 1773-1779
Author(s):  
Shou Tao Lv ◽  
Ze Yang Dai ◽  
Jian Liu
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cooperative Spectrum Sensing ◽  
Cognitive Radios ◽  
Radio Networks ◽  
Secondary Source ◽  
Secondary Users ◽  
Primary Users ◽  
The Third ◽  
Detection Probabilities

In cognitive radio networks (CRNs), the secondary users (SUs) need to continuously detect whether the primary users (PUs) occupy the spectrum. In order to improve the spectrum sensing accuracy, a novel reliable cooperative spectrum sensing strategy based on the detection results relayed twice from the secondary relays (SRs) to the secondary source (SS), referred to as CSS-DRT, is proposed in this paper. In this scheme, the spectrum sensing slot is divided into four equal sub-slots. In the first and third sub-slots, the SS and SRs detect the PU by themselves. Then, in the second sub-slot, if the SRs that detect the PU during the first sub-slot are more than or equal to a prespecified quantity, the corresponding SRs will send their flag signals (FSs) to the SS while the others keep quiet, where the FS is narrowband and indicates that the PU is present. Otherwise, if the SRs that detect the PU during the first sub-slot are less than the prespecified quantity, all the SRs will keep quiet in the second sub-slot. Meanwhile, the SS detects the PU based on the received signals from the PU and SRs. And, the SS uses the same method as employed in the second sub-slot to detect the PU in the last sub-slot wherein the SRs send their FSs based on their detections made during the third sub-slot. Finally, an ultimate decision is made by the OR ruler based on the SS detection results obtained during the spectrum sensing slot. Besides, we derive the closed-form expressions of the false alarm and detection probabilities for the proposed CSS-DRT scheme. In the end, simulation and numerical results show that our proposed scheme can achieve better performance than the non-cooperative method and an existing cooperative spectrum sensing method.

scholarly journals An SETM Algorithm for Combating SSDF Attack in Cognitive Radio Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-9 ◽  
Author(s):  
S. Tephillah ◽  
J. Martin Leo Manickam
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Trust Management ◽  
Cooperative Spectrum Sensing ◽  
Radio Networks ◽  
Second Phase ◽  
Common Channel ◽  
Secondary Users ◽  
Management Algorithm ◽  
Different Types

Security is a pending challenge in cooperative spectrum sensing (CSS) as it employs a common channel and a controller. Spectrum sensing data falsification (SSDF) attacks are challenging as different types of attackers use them. To address this issue, the sifting and evaluation trust management algorithm (SETM) is proposed. The necessity of computing the trust for all the secondary users (SUs) is eliminated based on the use of the first phase of the algorithm. The second phase is executed to differentiate the random attacker and the genuine SUs. This reduces the computation and overhead costs. Simulations and complexity analyses have been performed to prove the efficiency and appropriateness of the proposed algorithm for combating SSDF attacks.

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