BLSBA-ED-SSO: Bivariate Lévy-stable bat algorithm-based energy detection for spectrum sensing optimization in cognitive radio networks (CRNs)

2020 ◽  
Vol 33 (7) ◽  
pp. e4321 ◽  
Author(s):  
Senthil Kumar Babu ◽  
C. Venkataramanan
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Bat Algorithm ◽  
Energy Detection ◽  
Radio Networks

scholarly journals Energy Detection-based Spectrum Sensing In Cognitive Radio Networks

2020 ◽  
Vol 3 (1) ◽  
pp. 576-582
Author(s):  
Anıl Merve Ay
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Energy Detection ◽  
Radio Networks

Bugüne kadar kablosuz iletişim teknolojileri sabit spektrum atama prensibi ile hazırlanmış ve kullanılmıştır. Sabit spektrum atama prensibine göre belirli frekans bandı sadece birincil (lisanslı) kullanıcılara ayrılmakta, birincil kullanıcılar tarafından kullanılmıyor olsa dahi ikincil (lisanssız) kullanıcıya tahsis edilememektedir. Bu ilke, frekans spektrumunun verimsiz kullanılmasına neden olmaktadır. Günümüzde kablosuz iletişim teknolojilerinin devamlı gelişmesi ve yaygın kullanımıyla beraber, sınırlı olan frekans spektrumu yetersiz kalmaktadır. Bilişsel Radyo Teknolojisi, dinamik spektrum erişim tekniğini kullanarak frekans spektrumun verimli kullanılmasını amaçlamaktadır. Bu bildiride Bilişsel Radyo Teknolojisi ile boş frekans bandları tespit edilerek birincil kullanıcılar tarafından kullanılmayan bandların ikincil kullanıcılara tahsis edilmesi, Enerji Tespiti ile Spektrum Algılama Yöntemi kullanılarak simülasyon ortamında incelenmiş ve sonuçları verilmiştir.

Energy Detection Based Spectrum Sensing Analysis of Cooperative Cognitive Radios under Different Fading Environments

2013 ◽  
pp. 50-73
Author(s):  
Deepti Kakkar ◽  
Mayank Gupta ◽  
Arun Khosla ◽  
Moin Uddin
Keyword(s):  
Cognitive Radio ◽  
Fading Channels ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Cooperative Spectrum Sensing ◽  
Fusion Rule ◽  
Energy Detection ◽  
Radio Networks ◽  
Amplify And Forward ◽  
Comparative Performance

This chapter discusses the detection performance of relay based cognitive radio networks. Relays are assigned in cognitive radio networks to transmit the primary user’s signal to cognitive coordinators or CPUs, thus achieving cooperative spectrum sensing. The purpose of the chapter is to provide mathematical analysis of energy detectors for dual hop networks. The soft fusion rule is used at the relays which acts as amplify and forward relays. For the detection purpose, the energy detector is employed at the cognitive coordinator. In the ending sections, sensing performance is analyzed for different fading channels in the MATLAB environment and simulation results present comparative performance of various relay conditions with concluding remarks.

Improved Detection Performance of Energy Detection based Spectrum Sensing in Cognitive Radio Networks

2021 ◽  
Vol 11 ◽  
Author(s):  
Chilakala Sudhamani ◽  
Ashutosh Saxxena ◽  
Vunnava Aswini
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Detection Method ◽  
Detection Threshold ◽  
Threshold Energy ◽  
Energy Detection ◽  
Radio Networks ◽  
Detection Performance ◽  
Threshold Method

Background: In cognitive radio networks, spectrum sensing plays an important role in identifying the underutilized spectrum bands. Conventional spectrum sensing using energy detection method uses single detection threshold, which degrades the detection performance. Method: Therefore double detection threshold has been proposed for spectrum sensing in the literature to improve the detection performance, but the performance depends on the region between two thresholds termed as confusion state. Hence to improve the overall detection performance new re-sensing scheme has been proposed in this paper by varying the difference between thresholds by an improvement factor K. Results: The proposed method improves the detection performance compared to the single threshold method and double threshold method. Conclusion: Simulation results show that the proposed method operates better than the single threshold energy detection method and improves the detection performance at low signal to noise ratios.

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