Threshold selection analysis of spectrum sensing for cognitive radio network with censoring based imperfect reporting channels

Kumar, Alok; Pandit, Shweta; Singh, Ghanshyam

doi:10.1007/s11276-020-02488-9

“…In [17], to detect the malicious user, K Nearest Neighbor K-NN, has been used, where without any knowledge of the malicious user, detection can be made. In [18] & [19], advanced K-NN and Dempster-Shafer (D-S) were implemented to detect malicious SUs in the PU boundary coverage. In [20], a discussion on Cognitive Radio Sensor Networks (CRNS), where some general implementation issues with CRNS has been discussed.…”

Section: Literature Surveymentioning

confidence: 99%

Identifying Malicious Secondary User Presence Within Primary User Range in Cognitive Radio Networks

Brinda¹,

Bhuvaneshwari

²

2021

Wireless Pers Commun

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Cognitive Radio (CR) is a widely used wireless radio communication to utilize the available spectrum space of licensed users (Primary Users) efficiently. The primary Users (PU) unused spectrum space for a particular period is allocated to the Secondary Users (SU). The most alarming issue in this assignment is security, in which the secondary users disguise as primary users and misuses the allotted spectrum. To address this issue, in this paper we have proposed the Boundary detection method that uses the estimated location of each SU, which is obtained using the Recurrent Neural Network algorithm, to determine the boundary of PU coverage. Then Malicious User Detection by Ordering (MUDO) methodology is proposed, in which all secondary users are ordered, and based on the orders the SUs are paired with corresponding PUs. The SUs with the least orders are discarded as they might be malicious users. The proposed methodology possesses higher detection speed and precise detection thereby enhancing the performance of Cognitive Radio (CR).

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“…In [17], to detect the malicious user, K Nearest Neighbor K-NN, has been used, where without any knowledge of the malicious user, detection can be made. In [18] & [19], advanced K-NN and Dempster-Shafer (D-S) were implemented to detect malicious SUs in the PU boundary coverage. In [20], a discussion on Cognitive Radio Sensor Networks (CRNS), where some general implementation issues with CRNS has been discussed.…”

Section: Literature Surveymentioning

confidence: 99%

Identifying malicious secondary user presence within Primary User Range in Cognitive Radio Networks

Varatharajan

¹

,

Mariappan²

2021

Preprint

0

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Cognitive Radio (CR) is a widely used wireless radio communication to utilize the available spectrum space of licensed users (Primary Users) efficiently. The primary Users (PU) unused spectrum space for a particular period is allocated to the Secondary Users (SU). The most alarming issue in this assignment is security, in which the secondary users disguise as primary users and misuses the allotted spectrum. To address this issue, in this paper we have proposed the Boundary detection method that uses the estimated location of each SU, which is obtained using the Recurrent Neural Network algorithm, to determine the boundary of PU coverage. Then Malicious User Detection by Ordering (MUDO) methodology is proposed, in which all secondary users are ordered, and based on the orders the SUs are paired with corresponding PUs. The SUs with the least orders are discarded as they might be malicious users. The proposed methodology possesses higher detection speed and precise detection thereby enhancing the performance of Cognitive Radio (CR).

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“…SS has two goals: first, CR users must get out of interfering negatively with PUs by moving to an open band to a reasonable level [ 13 , 14 , 15 ]. Second, to attain the essential throughput and QoS, CR users should effectively locate and utilize the spectrum gaps [ 16 , 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

Spectrum Sensing Based on Hybrid Spectrum Handoff in Cognitive Radio Networks

Vaduganathan,

Neware,

Falkowski-Gilski

et al. 2023

Entropy

31

0

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The rapid advancement of wireless communication combined with insufficient spectrum exploitation opens the door for the expansion of novel wireless services. Cognitive radio network (CRN) technology makes it possible to periodically access the open spectrum bands, which in turn improves the effectiveness of CRNs. Spectrum sensing (SS), which allows unauthorized users to locate open spectrum bands, plays a fundamental part in CRNs. A precise approximation of the power spectrum is essential to accomplish this. On the assumption that each SU’s parameter vector contains some globally and partially shared parameters, spectrum sensing is viewed as a parameter estimation issue. Distributed and cooperative spectrum sensing (CSS) is a key component of this concept. This work introduces a new component-specific cooperative spectrum sensing model (CSCSSM) in CRNs considering the amplitude and phase components of the input signal including Component Specific Adaptive Estimation (CSAE) for mean squared deviation (MSD) formulation. The proposed concept ensures minimum information loss compared to the traditional methods that consider error calculation among the direct signal vectors. The experimental results and performance analysis prove the robustness and efficiency of the proposed work over the traditional methods.

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Threshold selection analysis of spectrum sensing for cognitive radio network with censoring based imperfect reporting channels

Cited by 12 publications

References 54 publications

Identifying Malicious Secondary User Presence Within Primary User Range in Cognitive Radio Networks

Identifying Malicious Secondary User Presence Within Primary User Range in Cognitive Radio Networks

Identifying malicious secondary user presence within Primary User Range in Cognitive Radio Networks

Spectrum Sensing Based on Hybrid Spectrum Handoff in Cognitive Radio Networks

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