scholarly journals Fast Cooperative Energy Detection under Accuracy Constraints in Cognitive Radio Networks

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Shengliang Peng ◽  
Weibin Zheng ◽  
Renyang Gao ◽  
Kejun Lei
Keyword(s):  
Cognitive Radio ◽  
Cognitive Radio Networks ◽  
Energy Detection ◽  
Radio Networks ◽  
Detection Accuracy ◽  
Detection Time ◽  
Rule Based ◽  
Counting Rule ◽  
Accuracy Constraints ◽  
Cooperative Energy

Cooperative energy detection (CED) is a key technique to identify the spectrum holes in cognitive radio networks. Previous study on this technique mainly aims at improving the detection accuracy, while paying little attention to the performance of detection time. This paper concentrates on the issue of fast CED, which is achieved by minimizing its detection time subject to the constraints on detection accuracy. Firstly, the prevalent counting rule based CED algorithm is optimized. Taking the special cases of counting rule (AND rule and OR rule), for example, we show that detection time can be minimized by selecting an optimal number of secondary users. Moreover, we prove that OR rule is superior to AND rule in detection time, and thus OR rule based CED is faster than AND rule based CED. Then, a sequential test (ST) based CED algorithm is proposed to exploit the benefit of ST and detect primary user even faster. After analyzing its detection time, we illustrate that ST based CED is able to spend the minimal detection time in satisfying the accuracy constraints by choosing an optimal sample number. Simulation results are provided to verify the effectiveness of both fast algorithms discussed in this paper.

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.

scholarly journals Semi-supervised machine learning for primary user emulation attack detection and prevention through core-based analytics for cognitive radio networks

2019 ◽  
Vol 15 (9) ◽  
pp. 155014771986036 ◽  
Author(s):  
Sundar Srinivasan ◽  
KB Shivakumar ◽  
Muazzam Mohammad
Keyword(s):  
Machine Learning ◽  
Cognitive Radio ◽  
Cognitive Radio Networks ◽  
Primary User ◽  
Radio Networks ◽  
Attack Detection ◽  
Supervised Machine Learning ◽  
Machine Language ◽  
Detection Accuracy ◽  
Primary User Emulation

Cognitive radio networks are software controlled radios with the ability to allocate and reallocate spectrum depending upon the demand. Although they promise an extremely optimal use of the spectrum, they also bring in the challenges of misuse and attacks. Selfish attacks among other attacks are the most challenging, in which a secondary user or an unauthorized user with unlicensed spectrum pretends to be a primary user by altering the signal characteristics. Proposed methods leverage advancement to efficiently detect and prevent primary user emulation future attack in cognitive radio using machine language techniques. In this paper novel method is proposed to leverage unique methodology which can efficiently handle during various dynamic changes includes varying bandwidth, signature changes etc… performing learning and classification at edge nodes followed by core nodes using deep learning convolution network. The proposed method is compared with that of two other state-of-art machine learning-based attack detection protocols and has found to significantly reduce the false alarm to secondary network, at the same time improve the overall detection accuracy at the primary network.

Sign in / Sign up

Export Citation Format

Share Document