Robust Spectrum Sensing via Double-Sided Neighbor Distance Based on Genetic Algorithm in Cognitive Radio Networks

Gul, Noor; Khan, Muhammad Sajjad; Kim, Junsu; Kim, Sumin

doi:10.1155/2020/8876824

Cited by 10 publications

(7 citation statements)

References 32 publications

(34 reference statements)

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“…The two-level securities were provided for the users during spectrum sensing in cognitive radio networks [41]. The genetic algorithm (GA) based filtration of false sensing reports was proposed in this paper [42]. The major problem of this research is defined as follows:…”

Section: Problem Statementmentioning

confidence: 99%

BlockCRN-IoCV: Secure Spectrum Access and Beamforming for Defense Against Attacks in mmWave Massive MIMO CRN in 6G Internet of Connected Vehicles

Pari

Jaisankar

2022

IEEE Access

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Cognitive Radio (CR) is a wireless communication system that is used for intelligent vehicles to solve spectrum scarcity and improve the utilization of the spectrum. However, spectrum sensing and data sharing are difficult due to the presence of malicious nodes which degrades the performance. To overcome these issues, we proposed the BlockCRN-IoCV method which includes authentication, density aware clustering, dual agent based spectrum access and secure beamforming. Here, authentication is performed for both Primary Users (PUs) and Secondary Users (SUs) using the Hybrid Advanced Encryption Standard and Hyper-elliptic Curve Cryptography (AES-HCC) algorithm by considering ID, PUF and location which ensures the legitimacy of the users. To address the mobility of the vehicle we perform density aware clustering using Density aware Dynamic Radius Clustering (DADRC) by considering location, distance and direction for increasing throughput. After completing clustering, we perform efficient spectrum access by using the Dual Agent based Twin Delayed (DA-TD3) algorithm which includes two agents, the first agent performs spectrum sensing by considering SNR, noise level and trust, and the second agent performs spectrum allocation by considering Channel State Information (CSI), in which the CSI is predicted by Quasi-Newton Iterative Unscented Kalman Filter (QNIUKF) algorithm for effective data transmission. Finally, secure beamforming is performed using Bi-Gated Recurrent Neural Network (BiGRU-CapsNet) by considering CSI, beam score, array factor, and direction of angle. The simulation is carried out by OMNET++ and SUMO simulation tools and the performance of this work is evaluated by throughput, packet delivery ratio, SNR, detection accuracy, BER, and delay. The simulation result shows that the proposed work achieves superior performance compared to existing work for secure spectrum sensing and beamforming.

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Section: Problem Statementmentioning

confidence: 99%

BlockCRN-IoCV: Secure Spectrum Access and Beamforming for Defense Against Attacks in mmWave Massive MIMO CRN in 6G Internet of Connected Vehicles

Pari

Jaisankar

2022

IEEE Access

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“…where R s is the source data rate of the primary signal s(n), N B is the number of backscattered bits, and N tot is the total number of transmitted bits. P x b are obtained in (30), (38), and (43).…”

Section: Average Achievable Ratementioning

confidence: 99%

“…In CR networks, sensing the primary system and reporting the sensing results to a secondary fusion node are important issues. Accordingly, there are several studies on the performance improvements in primary sensing [38] and reporting [39]. Recently, there are also a few studies on ABC systems under CR environments [40][41][42].…”

Section: Introductionmentioning

confidence: 99%

Multi-Tag Selection in Cognitive Ambient Backscatter Communications for Next-Generation IoT Networks

Kim

2022

Wireless Communications and Mobile Computing

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In this paper, we propose multi-tag selection schemes to improve achievable rate of a secondary ambient backscatter communication (ABC) system with multiple tags and a single power beacon in cognitive radio (CR) environments. Using the secondary power beacon, which plays a role as radio frequency (RF) source when there is no ambient RF source signals, with spectrum sensing capability, the secondary ABC system determines operation modes according to activity of the licensed primary system. When the primary system is active, a tag is randomly selected due to a lack of channel information, while when it is inactive, the best tag maximizing achievable rate is selected using the secondary power beacon. Moreover, we apply a successive interference cancellation (SIC) technique when the secondary power beacon is able to work. The performance of the proposed multi-tag selection schemes is mathematically analyzed in terms of bit error rate (BER) and achievable rate. Finally, the performance of the proposed schemes is evaluated through simulations in terms of BER and achievable rate. It is shown that our approach can significantly outperform a conventional ABC system in CR environments, especially under a low-active primary system regime.

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“…Thus, to tackle these challenges, it is more appropriate to utilize cooperative spectrum sensing (CSS) [13][14][15]. In the centralized CSS, a central base station, such as fusion center (FC) receives sensing information from the SUs in the hard or soft modes for final decision about the presence or absence of PU [16,17]. The hard decision fusion (HDF) often employs logical AND, logical OR, and majority voting schemes [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Optimal Cooperative Spectrum Sensing Based on Butterfly Optimization Algorithm

Abbasi¹,

Jamil²,

Chen³

2022

Computers, Materials &Amp; Continua

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Since the introduction of the Internet of Things (IoT), several researchers have been exploring its productivity to utilize and organize the spectrum assets. Cognitive radio (CR) technology is characterized as the best aspirant for wireless communications to augment IoT competencies. In the CR networks, secondary users (SUs) opportunistically get access to the primary users (PUs) spectrum through spectrum sensing. The multipath issues in the wireless channel can fluster the sensing ability of the individual SUs. Therefore, several cooperative SUs are engaged in cooperative spectrum sensing (CSS) to ensure reliable sensing results. In CSS, security is still a major concern for the researchers to safeguard the fusion center (FC) against abnormal sensing reports initiated by the malicious users (MUs). In this paper, butterfly optimization algorithm (BOA)-based soft decision method is proposed to find an optimized weighting coefficient vector correlated to the SUs sensing notifications. The coefficient vector is utilized in the soft decision rule at the FC before making any global decision. The effectiveness of the proposed scheme is compared for a variety of parameters with existing schemes through simulation results. The results confirmed the supremacy of the proposed BOA scheme in both the normal SUs' environment and when lower and higher SNRs information is carried by the different categories of MUs.

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Robust Spectrum Sensing via Double-Sided Neighbor Distance Based on Genetic Algorithm in Cognitive Radio Networks

Cited by 10 publications

References 32 publications

BlockCRN-IoCV: Secure Spectrum Access and Beamforming for Defense Against Attacks in mmWave Massive MIMO CRN in 6G Internet of Connected Vehicles

BlockCRN-IoCV: Secure Spectrum Access and Beamforming for Defense Against Attacks in mmWave Massive MIMO CRN in 6G Internet of Connected Vehicles

Multi-Tag Selection in Cognitive Ambient Backscatter Communications for Next-Generation IoT Networks

Optimal Cooperative Spectrum Sensing Based on Butterfly Optimization Algorithm

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