A Cooperative Spectrum Detection Technique in Non-Gaussian ...


A Cooperative Spectrum Detection Technique in Non-Gaussian ...

where Γ(.)

is the gamma function and I


(.) is the vth-order modified Bessel function of the first

kind. From (12) the false alarm probability and the detection probability of Rao detection are given


where Q a ∫ ∞ 1 1 2

( ) = exp( − t ) dt






= P(

T > γ | H ) 2Q(

γ )





3. Primary User Detection based on Multiuser Cooperation




PD = P(


> γ | H ) = Q(

γ − λ)

+ Q(

γ + λ)


A simulated model of CR networks can be described as Fig.1, where CUs (cognitive user) are

random distribution, CU1, CU2 and CU3 suffering different shadowing influence will receive weak

PU signal, which may give a wrong decision. If these CUs transmit their own data on the certain

spectrum paragraph, they likely affect the PU (primary user) receiver’s results, and create

interference to the PU system inevitably. CU4 and CU3 could receive the transmitter signal within

the radius scope of primary transmitter's emissive power.


Figure1. Cooperative spectrum sensing model

Based on the above detection scenario, the reliability that some single cognitive user, such as

CU1,CU2,CU3, detect available spectrum will be low, and thus some of these CUs are selected to

take part in spectrum detection at the same time, which can improve the detection performance.

Figure2. Block diagram of cooperative spectrum detection based on Rao

Fig.2 is the block diagram of the proposed cooperative spectrum detection based on the Rao

detector, where M cognitive users take part in cooperation. Cognitive users transmit local detection

results u = [ u1, u2... u M

] and SNR = [ SNR1, SNR2... SNR M

] to the fusion center (FC) after the estimation of

ε and the Rao detection. The FC chooses the users whose SNR are larger than the average SNR


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