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

where Γ(.)
is the gamma function and I
v
(.) 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
by
where Q a ∫ ∞ 1 1 2
( ) = exp( − t ) dt
a
2π
2
P
'
'
= P(
T > γ | H ) 2Q(
γ )
(13)
FA R
0
=
3. Primary User Detection based on Multiuser Cooperation
'
'
'
PD = P(
TR
> γ | H ) = Q(
γ − λ)
+ Q(
γ + λ)
(14)
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.
1
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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