Aerospace and Electronic Systems Magazine May 2018 - 24
Real-Time Adaptive Spectrum Sensing
the PU/SUs (for the downlink case, the interference created by the
PUs to the SUs is regarded while for the uplink case, it is the opposite). Spectrum sensing is not considered as a separate function inside the framework, but a type of energy detection is implemented
as a part of its main decision-making algorithm.
These publications demonstrate relevant application scenarios,
but they do not consider the actual detection model in realistic environments. Detectors of weak PU signals, which have been validated via practical implementations, are necessary.
A spectrum sharing architecture implemented using a Wi-Fi/
Long-Term Evolution testbed is examined in [17]. It includes software which controls the operation of the system and similarly to
the previously mentioned solutions, it does not focus on the process of spectrum sensing but utilizes a simple ED.
So far there has only been one experimental complete CR
implementation [2] which has been tested in the premise of a realworld primary network. The CR network operates under specific
power constraints to ensure that the incumbent users will not experience quality of service degradation. A mechanism is implemented which allows the CR base stations to be notified when a PU
needs to return to a portion of the spectrum so that the CR network
can be reconfigured to vacate it. Thus, the spectrum sensing functionality is simplified.
The fact that these architectures and implementations consider
primarily, simple spectrum sensing techniques further highlights
the necessity of evaluation of more complex and accurate detectors
implemented in realistic scenarios.
SYSTEM MODEL
CYCLOSTATIONARY DETECTION-BASED SPECTRUM SENSING
Although the cyclostationary detector requires much greater computational complexity than the ED, its advantage is that as the
noise does not exhibit cyclostationary properties, the signal could
be detected even if it is very weak. Results from various research
studies ([7], [8], [9] [36], [37], [38]) have shown that the method
has very good detection accuracy in low SNR (the range −20 to 0
dB is of particular interest). In many of them, the SCF is compared
against the decision threshold λ, however, in our implementation
we use the CAF. The estimated CAF for a specific cyclic frequency
α and a time lag τ is defined as in [27]:
1
Rˆ xα (τ ) =
M
M −1
R ( n,τ ) e
n=0
x
− j 2πα n
,
Thus, the test statistic is given by (3):
2
Λ (α ) = Rˆ xα λ .
The detector decides that the spectrum is occupied by the PU
(H1) if the statistic is larger than the threshold λ, otherwise it is
available for utilization by the SU (H0). The derivations of the
probabilities of detection and of false-alarm are based on the assumptions that the noise is AWGN with zero mean and variance
while the PU signal is a random variable with zero mean and variance σ w2 while the PU signal is a random variable with zero mean
and variance σ s2.
The probability of false alarm is defined in the following way:
Pfa = e
−λ
MN
2σ w4
.
(4)
Therefore, we can set the Pfa as a constant and calculate the
decision threshold as in (5):
λ=−
( ).
2σ w4 ln Pfa
We should note, here, that because of our implementation, the
variance of the noise is constant, and therefore, the threshold value
does not vary dramatically. Thus, utilizing a constant false-alarmrate method is equivalent to taking the threshold as a parameter,
on the basis of which the Pfa can be calculated. In comparison,
the dependency between the threshold and Pfa in the case of the
traditional energy detector [23] is similar but there, the number of
the observed samples is directly related to the received SNR, and
therefore the threshold is changing (it is inversely proportional to
the SNR).
Finally, we use the derived expression for the probability of
detection in a Rayleigh flat-fading channel. Here, it should be
considered on whether the common assumption about the distribution of the channel attenuation coefficient, is to be employed
or not. Preliminary measurements allowed us to estimate the distribution of h (and consequently, for h2) for our setting. It was
verified that the Rayleigh distribution is suitable for the gathered
data by examining the received samples using the Bayesian Information Criterion to find the best fit for them. Then we find the
mean of the distribution μ. With it added, the expression for Pd
from [27] is written as
Pd
Q1 f ,
where M is the observed number of samples and Rx(n, τ) is the
min h
f f
autocorrelation function of the signal x(n) with period n equal to Ts
2
2 2
w s h
(the signal period).
f
; f
MN
In order to reduce the computational complexity of the opera-
1 1
Rˆ xα (τ ) =
M N
max h
24
M −1 n + N −1
x (i ) x (i + τ ) e
n=0
i=n
*
− j 2πα n
.
(2)
(5)
MN
(1)
tion, the authors in [27] proposed a small modification, which establishes the overall solution as a single-cycle detector with sliding correlation (SCD-SC). The sliding window has a length of N
samples.
(3)
h2
e
dh 2 ,
Rˆ x h 2 .
(6)
CHANNEL ESTIMATION TECHNIQUE
For calculating the integral (6) over the distribution of h2 we need
to estimate the channel attenuation at each detection instance. In
order to estimate h, the following procedure is implemented. We
obtain the initial value of h by using (5) from [39]:
IEEE A&E SYSTEMS MAGAZINE
MAY - JUNE 2018
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