Optimizing Spectrum Sensing Time for Energy-Efficient CRSNs

2016 ◽  
Author(s):  
Fanhua Kong ◽  
Zilong Jin ◽  
Jinsung Cho ◽  
Seokhee Jeon ◽  
Sungwon Lee
Keyword(s):  
Spectrum Sensing ◽  
Energy Efficient ◽  
Sensing Time

A novel spectrum sensing scheme with sensing time optimization for energy-efficient CRSNs

2017 ◽  
Vol 24 (7) ◽  
pp. 2781-2794
Author(s):  
Fanhua Kong ◽  
Zilong Jin ◽  
Jinsung Cho ◽  
Ben Lee
Keyword(s):  
Spectrum Sensing ◽  
Energy Efficient ◽  
Time Optimization ◽  
Sensing Time

A hybrid model for energy efficient spectrum sensing in cognitive radio

2019 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mahua Bhowmik ◽  
P. Malathi P. Malathi
Keyword(s):  
Cognitive Radio ◽  
Prediction Model ◽  
Spectrum Sensing ◽  
Hybrid Model ◽  
Energy Efficient ◽  
Probability Of Detection ◽  
Content Type ◽  
Hybrid Prediction ◽  
Probability Of False Alarm ◽  
Sensing Time

Purpose Cognitive radio (CR) plays a very important role in enabling spectral efficiency in wireless communication networks, where the secondary user (SU) allows the licensed primary users (PUs). The purpose of this paper is to develop a prediction model for spectrum sensing in CR. Design/methodology/approach This paper proposes a hybrid prediction model, called krill-herd whale optimization-based actor critic neural network and hidden Markov model (KHWO-ACNN-HMM). The spectral bands are determined optimally using the proposed hybrid prediction model for allocating the spectrum bands to the PUs. For better sensing, the eigenvalue based on cooperative sensing used in CR. Finally, a hybrid model is designed by hybridizing KHWO-ACNN and HMM to enhance the accuracy of sensing. The predicted results of KHWO-ACNN and HMM are combined by a fusion model, for which a weighted entropy fusion is employed to determine the free spectrum available in CRs. Findings The performance of the prediction model is evaluated based on metrics, such as probability of detection, probability of false alarm, throughput and sensing time. The proposed spectrum sensing method achieves maximum probability of detection of 0.9696, minimum probability of false alarm rate as 0.78, minimum throughput of 0.0303 and the maximum sensing time of 650.08 s. Research implications The proposed method is useful in various applications, including authentication applications, wireless medical networks and so on. Originality/value A hybrid prediction model is introduced for energy efficient spectrum sensing in CR and the performance of the proposed model is evaluated with the existing models. The proposed hybrid model outperformed the other techniques.

scholarly journals Energy-Efficient Cluster-Based Cooperative Spectrum Sensing in a Multiple Antenna Cognitive Radio Network

2021 ◽  
pp. 176-185
Author(s):  
Samson I. Ojo ◽  
◽  
Zachaeus K. Adeyemo ◽  
Damilare O. Akande ◽  
Ayobami O. Fawole
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Energy Efficient ◽  
Cooperative Spectrum Sensing ◽  
Cluster Head ◽  
Fusion Rule ◽  
Probability Of Detection ◽  
Multiple Antenna ◽  
Secondary Users ◽  
Sensing Time

Spectrum Hole Detection (SHD) is a major operation in a Cognitive Radio (CR) network to identify empty spectrum for maximum utilization. However, SHD is often affected by multipath effects resulting in interference. The existing techniques used to address these problems are faced by poor detection rate, long sensing time and bandwidth inefficiency. Hence, this paper proposes a cluster-based Energy-Efficient Multiple Antenna Cooperative Spectrum Sensing (EEMACSS) for SHD in CR networks using Energy Detector (ED) with a modified combiner. Multiple secondary users are used to carry out local sensing using ED in multiple antenna configurations. The local sensing results are combined at the cluster head using majority fusion rule to determine the sensing results at each cluster. The sensing results from individual cluster are combined to determine the global sensing result using OR fusion rule. The proposed EEMACSS is evaluated using Probability of Detection (PD), Sensing Time (ST) and Spectral Efficiency (SE) by comparing with existing techniques. The results reveal that the proposed technique shows better performance.

scholarly journals Sensing Time Optimization and Power Control for Energy Efficient Cognitive Small Cell With Imperfect Hybrid Spectrum Sensing

2017 ◽  
Vol 16 (2) ◽  
pp. 730-743 ◽  
Author(s):  
Haijun Zhang ◽  
Yani Nie ◽  
Julian Cheng ◽  
Victor C. M. Leung ◽  
Arumugam Nallanathan
Keyword(s):  
Power Control ◽  
Spectrum Sensing ◽  
Energy Efficient ◽  
Small Cell ◽  
Time Optimization ◽  
Sensing Time

Parallel Cyclostationarity-Exploiting Algorithm for Energy-Efficient Spectrum Sensing

2014 ◽  
Vol E97.B (2) ◽  
pp. 326-333 ◽  
Author(s):  
Arthur D.D. LIMA ◽  
Carlos A. BARROS ◽  
Luiz Felipe Q. SILVEIRA ◽  
Samuel XAVIER-DE-SOUZA ◽  
Carlos A. VALDERRAMA
Keyword(s):  
Spectrum Sensing ◽  
Energy Efficient

An Energy Efficient Spectrum Aware Routing for Cognitive Radio Networks (CRNs)

2020 ◽  
Vol 10 ◽  
Author(s):  
Dileep Reddy Bolla ◽  
Jijesh J J ◽  
Mahaveer Penna ◽  
Shiva Shankar
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Energy Efficient ◽  
Research Work ◽  
Spectrum Management ◽  
Residual Energy ◽  
Radio Networks ◽  
Traffic Load ◽  
The Impact

Back Ground/ Aims:: Now-a-days in the Wireless Communications some of the spectrum bands are underutilized or unutilized; the spectrum can be utilized properly by using the Cognitive Radio Techniques using the Spectrum Sensing mechanisms. Objectives:: The prime objective of the research work carried out is to achieve the energy efficiency and to use the spectrum effectively by using the spectrum management concept and achieve better throughput, end to end delay etc., Methods:: The detection of the spectrum hole plays a vital role in the routing of Cognitive Radio Networks (CRNs). While detecting the spectrum holes and the routing, sensing is impacted by the hidden node issues and exposed node issues. The impact of sensing is improved by incorporating the Cooperative Spectrum Sensing (CSS) techniques. Along with these issues the spectrum resources changes time to time in the routing. Results:: All the issues are addressed with An Energy Efficient Spectrum aware Routing (EESR) protocol which improves the timeslot and the routing schemes. The overall network life time is improved with the aid of residual energy concepts and the overall network performance is improved. Conclusion:: The proposed protocol (EESR) is an integrated system with spectrum management and the routing is successfully established to communication in the network and further traffic load is observed to be balanced in the protocol based on the residual energy in a node and further it improves the Network Lifetime of the Overall Network and the Individual CR user, along with this the performance of the proposed protocol outperforms the conventional state of art routing protocols.

scholarly journals Efficient Approximations for Optimization of N-Out-of-K Rule for Heterogeneous Cognitive Radio Networks

2021 ◽  
Vol 11 (7) ◽  
pp. 3083
Author(s):  
Youheng Tan ◽  
Xiaojun Jing
Keyword(s):  
Cognitive Radio ◽  
Computational Complexity ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Radio Networks ◽  
Closed Form Expression ◽  
Sampling Point ◽  
Mutual Cooperation ◽  
Communication Time ◽  
Sensing Time

Spectrum sensing (SS) has attracted much attention due to its important role in the improvement of spectrum efficiency. However, the limited sensing time leads to an insufficient sampling point due to the tradeoff between sensing time and communication time. Although the sensing performance of cooperative spectrum sensing (CSS) is greatly improved by mutual cooperation between cognitive nodes, it is at the expense of computational complexity. In this paper, efficient approximations of the N-out-of-K rule-based CSS scheme under heterogeneous cognitive radio networks are provided to obtain the closed-form expression of the sensing threshold at the fusion center (FC), where the false alarm probability and its corresponding detection probability are approximated by the Poisson distribution. The computational complexity required to obtain the optimal sensing threshold at the FC has greatly decreased and theoretical derivations state that the approximation error is negligible. The simulations validate the effectiveness of the proposed scheme.

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