• Journal of Satellite, Information and Communications
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• v.9 no.4
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• pp.20-25
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• 2014

In this paper, we provide efficient spectrum sensing technology for smooth use of frequency and energy charge of multi-copter. The proposed structures focus on improving performance of spectrum sensing that is based on Ad-hoc network. First, we explain basic principles and disadvantages of cooperative spectrum sensing and ad-hoc based spectrum sensing. To solve these problems, in this paper, we employ the beamforming technology that guarantees higher transmit primary users' signal power to secondary users in ad-hoc network. The performance of proposed algorithm is analyzed in terms of detection probabilities, and the results of this paper can be applied to the various ad-hoc based Cognitive Radio system.

• Journal of Satellite, Information and Communications
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• v.10 no.1
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• pp.49-55
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• 2015

In cognitive radio network (CRN), spectrum sensing is an elementary level of technology for non-interfering to licensed user. Required sample number for spectrum sensing is directly related to the throughput of secondary user and makes the tradeoff between the throughput of secondary user and interference to primary user. Required spectrum sensing sample is derived from required false alarm, detection probability and minimum required SNR of primary user (PU). If we make clustering and minimize the required transmission boundary of secondary user (SU), we can relax the required PU SNR for spectrum sensing because the required SNR for PU signal sensing is related to transmission range of SU. Therefore we can achieve efficient throughput of CRN by minimizing spectrum sensing sample. For this, we design the tradeoff between gain and loss could be obtained from clustering, according to the size of cluster members through game theory and simulation results confirm the effectiveness of the proposed method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.7
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• pp.1951-1975
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• 2023

Recent advances in Cognitive Radio Networks (CRN) have elevated them to the status of a critical instrument for overcoming spectrum limits and achieving severe future wireless communication requirements. Collaborative spectrum sensing is presented for efficient channel selection because spectrum sensing is an essential part of CRNs. This study presents an innovative cooperative spectrum sensing (CSS) model that is built on the Firefly Algorithm (FA), as well as machine learning artificial neural networks (ANN). This system makes use of user grouping strategies to improve detection performance dramatically while lowering collaboration costs. Cooperative sensing wasn't used until after cognitive radio users had been correctly identified using energy data samples and an ANN model. Cooperative sensing strategies produce a user base that is either secure, requires less effort, or is faultless. The suggested method's purpose is to choose the best transmission channel. Clustering is utilized by the suggested ANN-FA model to reduce spectrum sensing inaccuracy. The transmission channel that has the highest weight is chosen by employing the method that has been provided for computing channel weight. The proposed ANN-FA model computes channel weight based on three sets of input parameters: PU utilization, CR count, and channel capacity. Using an improved evolutionary algorithm, the key principles of the ANN-FA scheme are optimized to boost the overall efficiency of the CRN channel selection technique. This study proposes the Artificial Neural Network with Firefly Algorithm (ANN-FA) for cognitive radio networks to overcome the obstacles. This proposed work focuses primarily on sensing the optimal secondary user channel and reducing the spectrum handoff delay in wireless networks. Several benchmark functions are utilized We analyze the efficacy of this innovative strategy by evaluating its performance. The performance of ANN-FA is 22.72 percent more robust and effective than that of the other metaheuristic algorithm, according to experimental findings. The proposed ANN-FA model is simulated using the NS2 simulator, The results are evaluated in terms of average interference ratio, spectrum opportunity utilization, three metrics are measured: packet delivery ratio (PDR), end-to-end delay, and end-to-average throughput for a variety of different CRs found in the network.