Cross entropy algorithm for improved soft fusion-based cooperative spectrum sensing in cognitive radio networks

El‐Saleh, Ayman A.; Albreem, Mahmoud A.; Ahad, Tauseef Rasheq; Raquib, Waziha

doi:10.1109/menacomm.2018.8371020

Cited by 3 publications

(2 citation statements)

References 23 publications

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“…One of the critical issues in obtaining the optimal fusion weights and beamforming weights is that the objective function of the optimization task is usually highly nonlinear and non-convex, which limits the use of classical optimization techniques. Thus, many heuristic optimization strategies have been employed for the SS in CRN [36][37][38][39][40][41][42]. In [36], genetic algorithm-assisted optimization for finding the optimal fusion combining is proposed.…”

Section: Introductionmentioning

confidence: 99%

“…In [38], a modified genetic algorithm (GA) was proposed for SS which employs two additional constraints to limit the number of GA solutions and to reduce the size of the search space, which is termed as genetic algorithm-multi parent crossover (GA-MPC). Another method called cross-entropy has been successfully employed for the SS in [39]. Besides that, the particle swarm optimization (PSO) algorithm has recently shown very effective performance in CRN [42].…”

Section: Introductionmentioning

confidence: 99%

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Optimized Statistical Beamforming for Cooperative Spectrum Sensing in Cognitive Radio Networks

Al‐Saggaf

Jafarian²,

Alrefaei

et al. 2023

Mathematics

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In cognitive radio (CR), cooperative spectrum sensing (CSS) employs a fusion of multiple decisions from various secondary user (SU) nodes at a central fusion center (FC) to detect spectral holes not utilized by the primary user (PU). The energy detector (ED) is a well-established technique of spectrum sensing (SS). However, a major challenge in designing an energy detector-based SS is the requirement of correct knowledge for the distribution of decision statistics. Usually, the Gaussian assumption is employed for the received statistics, which is not true in real practice, particularly with a limited number of samples. Another big challenge in the CSS task is choosing an optimal fusion strategy. To tackle these issues, we have proposed a beamforming-assisted ED with a heuristic-optimized CSS technique that utilizes a more accurate distribution of decision statistics by employing the characterization of the indefinite quadratic form (IQF). Two heuristic algorithms, genetic algorithm with multi-parent crossover (GA-MPC) and constriction factor particle swarm-based optimization (CF-PSO), are developed to design optimum beamforming and optimum fusion weights that can maximize the global probability of detection pd while constraining the global probability of false alarm pf to below a required level. The simulation results are presented to validate the theoretical findings and to asses the performance of the proposed algorithm.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimized Statistical Beamforming for Cooperative Spectrum Sensing in Cognitive Radio Networks

Al‐Saggaf

Jafarian²,

Alrefaei

et al. 2023

Mathematics

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Cluster mechanism for sensing data report using robust collaborative distributed spectrum sensing

et al. 2021

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Cooperative Fusion Architecture-based Distributed Spectrum Sensing Under Rayleigh Fading Channel

Mokhtar

Saeed

Alhumyani

2022

Wireless Pers Commun

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Cognitive radio (CR) is one of the most promising technology soon due to the scarcity of the spectrum, especially at microwave band. CR faces massive resistance from the industry because of the potential interference caused by the secondary users. Spectrum sensing forms an important functionality for CR systems. However, such detection performance is usually compromised by shadowing and fading channel conditions. Cooperative sensing is one of the crucial solutions to overcome degraded detection performance. To improve the sensing performance and reduce the reporting error, a distributed architecture for processing and fusion of sensing information is proposed in this work. In dense network scenarios, the decision fusion for cooperated users could be complex and reported sensing traffic may require large bandwidth. This paper proposes a new distributed detection and adapted threshold based on controlled false alarm probability to improve sensing reliability and efficiency in a highly Rayleigh faded environment. A distributed detection is developed by selecting fusion nodes (FN) that are dynamically selected from a group of nodes. The detection threshold is calculated adaptively using the link quality indicator (LQI) of the sensing channel. Moreover, the proposed method can significantly minimize the typically transmitted bits in the reporting channel. The paper also discussed in detail the design parameter of the CR number on the performance of fusion values. The simulation analysis shows that the performance of the distributed cooperative sensing (DCS) process is considerably improved by the adapted threshold.The numerical results demonstrated that the error was remarkably minimized. The ROC curve of the sensing process is notably improved for detection probability and false alarm probability, respectively. Finally, it was shown that the requirement of sensitivity can be greatly improved up to 0.95.

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Cross entropy algorithm for improved soft fusion-based cooperative spectrum sensing in cognitive radio networks

Cited by 3 publications

References 23 publications

Optimized Statistical Beamforming for Cooperative Spectrum Sensing in Cognitive Radio Networks

Optimized Statistical Beamforming for Cooperative Spectrum Sensing in Cognitive Radio Networks

Cluster mechanism for sensing data report using robust collaborative distributed spectrum sensing

Cooperative Fusion Architecture-based Distributed Spectrum Sensing Under Rayleigh Fading Channel

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