Energy Detection Based Cooperative Spectrum Sensing in Cognitive Radio Networks

Abstract

Detection performance of an energy detector used for cooperative spectrum sensing in a cognitive radio network is investigated over channels with both multipath fading and shadowing. The analysis focuses on two fusion strategies: data fusion and decision fusion. Under data fusion, upper bounds for average detection probabilities are derived for four scenarios: 1) single cognitive relay; 2) multiple cognitive relays; 3) multiple cognitive relays with direct link; and 4) multi-hop cognitive relays. Under decision fusion, the exact detection and false alarm probabilities are derived under the generalized " -outof-" fusion rule at the fusion center with consideration of errors in the reporting channel due to fading. The results are extended to a multi-hop network as well. Our analysis is validated by numerical and simulation results. Although this research focuses on Rayleigh multipath fading and lognormal shadowing, the analytical framework can be extended to channels with Nakagami-multipath fading and lognormal shadowing as well.

Optimization in Cooperative Spectrum Sensing Techniques of CRN

“…Cooperative sensing is done byfusion the sensing data of individual secondary users andmake a final decision at the secondary BS. To minimize the transmission overhead of the sensing data, every secondary users will make their own sensing decision and transmit their one-bit decision to the secondary BS for fusion The main advantage of this method is the fact that it needs limited bandwidth [10] We have a number of G (or r = 1, 2. . .…”

Analysis of Energy Detector in Cooperative Relay Networks for Cognitive Radios

“…It is worth noting that we obtained here the exactP d,rel expression which was otherwise analytically impossible to obtain using the MGF method of [10], hence an upper bound for relay linkP d was proposed in [10]. Furthermore, the obtained analytical expression can easily be implemented in many mathematical software packages such as Mathematica or MATLAB.…”

“…Finally, the exact expression ofP d can be obtained by using [16, was otherwise analytically impossible to obtain using the MGF method of [10]. Furthermore, the obtained analytical expression can easily be implemented in many mathematical software packages such as Mathematica or MATLAB.…”

“…Consequently in the context of cognitive radio networks, an analytical upper bound on average detection probability of the cooperative spectrum sensing with amplify-forward (AF) based blind (a.k.a. fixedgain) cognitive relays was carried out using the MGF method in [10]. Since the MGF method requires the computation of complicated contour integrations, therefore, in [11], [12], authors utilized a fairly simpler PDF method to obtain upperbounds on average detection probability for variable-gain AF relay networks with MRC and SC respectively.…”

Cyclostationary Feature Detection in Cognitive Radio for Ultra-Wideband Communication Using Cooperative Spectrum Sensing

“…The relaying method involved is amplifying and forward. At the fusion center, any of the majority combining rules, AND, OR rules [11] etc can be used. This paper focuses on the results obtained using AND rule for data fusion.…”