An Alternative Energy Detection Using Sliding Window for Cognitive ...

An Alternative Energy Detection Using Sliding
Window for Cognitive Radio System
Young Min Kim, Guanbo Zheng, Sung Hwan Sohn and Jae Moung Kim
The Graduate school of Information Technology & Telecommunications, Inha University
Incheon, South Korea 402-751
ymym@hanmail.net, gbzheng@gmail.com, kittisn@naver.com, jaekim@inha.ac.kr
Abstract ⎯ Cognitive radio is one of the most effective
techniques to improve the spectrum utilization efficiency. To
implement the cognitive radio, spectrum sensing is considered as
the key functionality because secondary users should identify the
spectrum holes and utilize them efficiently without causing
interference to primary users. In generally, there are three major
spectrum sensing methods, including matched filter, energy
detection and feature detection. The most common and simplest
method is energy detection. However, the conventional energy
detection suffers from large degradation in performance under
low SNR environment. In this paper, we proposed alternative
energy detection which can estimate the exact power of primary
user without the noise effect and lead to the better performance.
In order to reduce the effect of noise fluctuation, the sliding
window function is utilized to assist the proposed detector. The
simulation result shows that our proposed method outperform
conventional one.
1. Introduction
Recently, our frequency utilization is increasingly expanded
due to the developing information technologies in all regions
of our life. More wireless communication systems are being
developed and deployed which require larger coverage, higher
data rate transmission and better quality to satisfy the large
increasing of customers in communication service.
Unfortunately, the spectrum resource is limited and almost all
frequency bands are fixedly allocated according to current
spectrum allocation policy, especially for the spectrum bands
below 3GHz frequency. The way for fully utilizing the limited
spectrum resource is becoming an open issue in wireless
communication. On the other hand, from Spectrum Policy
Task Force report of FCC (Federal Communications
Commission) on 2002, there are a lot of temporal and
geographic spectrum vacancies in the allocated spectrum,
which clearly indicates the inefficiency of the fixed license
spectrum policy, rather than the physical deficiency of
spectrum resource [1].
Cognitive Radio, the advanced software-defined radios
coined by Joseph Mitola, has been considered as the best
candidate technology to implement the opportunistic spectrum
sharing [2]. It is an intelligent wireless communication system
that can sense the spectral environment over a wide frequency
band and exploit this information to opportunistically provide
wireless service accesses that best meet the requirements of
customers. Thus, we can find out the spectrum holes and
utilize them to improve the spectrum efficiency through the
cognitive radio techniques.
As the secondary user, the fundamental requirement of
cognitive radio is to avoid interference to potential primary
users in the neighborhood. Spectrum sensing has been pointed
out as the key functionality in implementing cognitive radio.
In order to protect primary users without reservation, cognitive
radio should perform the spectrum sensing function, detect the
primary signals in occupying the spectrum bands and properly
respond to changes in its environment periodically. Recent
studies have proposed several methods, such as matched filter
[3, 4], energy based detector [5, 6] and cyclostationarity based
detector [7, 8].
As we know, conventional energy detector is the most
common method in signal detection. However, It has some
significant disadvantages. Thus, we proposed the alternative
energy detector in this paper to estimate the exact primary user
power without noise effect, which can lead to the better
detection performance. And the sliding window scheme is
utilized to assist the detector for better performance. The
particulars are explained by next part.
The reminder of this paper is organized as follows: in
section II, conventional energy detection is briefly reviewed.
Then our proposed algorithm is explored in section III. In
order to verify the proposed algorithm, the simulation result is
shown and discussed in section IV. Finally, we make a
conclusion about the paper in section V.
2. Conventional Energy Detection.
Energy detection is the most common method for spectrum
sensing because of its non-coherency and low complexity.
Conventional energy detector can be simply implemented like
spectrum analyzer, the block diagram of which is drawn in
figure 1.
Figure 1. Conventional Energy Detection
After analog to digital conversion of the received signal, we
can get the N frequency samples by N-point FFT. Then the
signal power is estimated through the simple calculation of
each sample in frequency domain. Finally, primary user signal

is declared to exist when the signal power is larger than the
threshold. The detection is generally described under the test
of the following two hypotheses:
1) H0 : x(n) = w(n), signal is absent
2) H1 : x(n) = s(n)+w(n), signal is present
where w(n) is additive white Gaussian noise with zero mean,
s(n) represents the received primary user signal.
However, there are some significant drawbacks. The main
problem is the defective effect due to the decrease of
difference between signal and noise level. Since it uses
predefined threshold for signal detection, which makes the
performance highly rely on the noise level. For the case of low
SNR environment or fading channel, the performance
degrades largely and makes the detector useless. Another
disadvantage is energy detector can not identify the type of
primary signal occupying the frequency band. It can not
distinguish one primary user from others. Thus, we proposed
the algorithm which can relieve the drawbacks and make the
better performance.
3. Alternative Energy Detection Algorithm
The proposed alternative energy detection algorithm, based
on optimal radiometer concept, is employed in this paper. In
order to detect the possible primary user signal, we first
observe the frequency spectrum of received signal, as shown
in figure 2. We suppose primary user has a perfect guard band,
where BWob is the observed bandwidth, BWeff is the effective
bandwidth only occupied by primary user, BWGB1 and BWGB2
are the guard band both sides of primary user.
Considering the signal spectrum mentioned above, we can
easily detect the signal of PU2 using the proposed detector.
The primary signal with noise exists together in BWeff, while
there is only noise power in BWGB1 and BWGB2. Therefore, if
we can find the location of BWGB1 and BWGB2, it is possible to
calculate the exact primary signal power without the noise part
and then decide the existence of the primary signal. The
proposed detection scheme is described in next steps.
Figure 2. The Observed Spectrum of the Received Signal
First, we can estimate the noise power in guard band BWGB1
which indicates the only noise part. Here the noise is
considered to be AWGN, which has the zero mean and
variance σ 2 . Using this value, we can get accurate noise power
in primary user occupancy band BWeff, as demonstrated in (1).
P ×
BW
eff
n,
eff = Pn
, GB
(1)
1 BWGB1
where Pn,eff is noise power in effective bandwidth, Pn,GB1 is
noise power in guard band 1. The guard band 2 can be
described the same as the guard band 1. Therefore, the exact
primary signal power in BWeff can be described by:
P s,
eff Ps
n,
eff − Pn
, eff
= + (2)
where Ps,eff is the only primary signal power except the noise
power in effective band (BWeff), Ps+n,eff is the primary signal
power adding the noise power in effective band (BWeff).
Therefore, we got the estimated value of only primary user’s
power Ps,eff. This is the ideal case for alternative energy
detector. However, the real signal spectrum does not have the
perfect guard band. Due to the interference, noise fluctuation
and out of band radiation, etc, it is difficult to identify the
guard band from the band occupied by primary signal. Thus,
we need to work on guard band identification. In this paper,
with the help of sliding window to decrease the noise effect,
we try to find the guard band by using the first threshold. If the
power sample is lower than threshold, the region is decided to
be in the guard band.
From the description above, an alternative energy detection
algorithm is proposed. The block diagram is shown in figure 3,
while the details are explained in the following parts.
A. Original Spectrum Shaping
This step is same to conventional energy detection based on
FFT. First, the received signal xa(t) is sampled with Nyquist
sampling rate in Analog to Digital Converter and changed to
the digital signal [9, 10].
Then, the received signal is changed to frequency domain
signal by N-point FFT, while the frequency sample X(k) by
can be described in the following by DFT equation:
X ( k)
1
= ∑ − N
n=
0
x(
n)
e
j2πnk / N
0 ≤ k ≤ N −1
The power sample P(k) is calculated by squaring each
frequency samples X(k).
B. Sliding Window
In order to reduce the defective effect due to noise level
fluctuation in real signal, the sliding window is employed to
assist the proposed detector. A sliding window algorithm
places a buffer between power samples for adding all sample
(3)

values of window size and averaging these. The sliding
window size is pre-defined and saved in the memory. After
averaging in total window, it is shifted to next sample up to
last sample. The power samples are passed through the sliding
window to get the smoother curve. The k-th window w(k) is
defined as:
N 1
wk ( ) = ∑ Pkn ( , )
(4)
N =
n 1
where N indicates the window size.
We can confirm this fact through the next example. When we
consider DTV signal as primary user, which is VSBmodulated.
The spectrum of DTV signal with SNR 10dB and -
5dB are shown in figure 4 and figure 5, respectively.
Obviously, the detection would be very difficult in low SNR
environment due to the significant fluctuation. After passing
the sliding window, the spectrum of DTV signal with SNR -
5dB is drawn in figure 6. Comparing this with figure 5, we can
see the performance is improved a lot after using sliding
window.
Magnitude
-35
-40
-45
-50
-55
-60
-65
-70
0 1 2 3 4 5 6 7 8 9 10
Frequency (MHz)
Figure 3. The Block Diagram of Alternative Energy Detector Using Sliding Window
Figure 4. The Spectrum of DTV Signal with SNR 10dB
Magnitude
Magnitude
-40
-45
-50
-55
-60
-65
-70
0 1 2 3 4 5 6 7 8 9 10
Frequency (MHz)
-98
-99
-100
-101
-102
-103
Figure 5. The Spectrum of DTV Signal with SNR -5dB
p g ( g )
-104
0 1 2 3 4 5 6 7 8 9 10
Frequency (MHz)
Figure 6. The Spectrum of DTV Signal with SNR -5dB
after Sliding Window
C. Guard Band Identification
This step in proposed algorithm is to identify the guard band,
which includes the noise part only. With the help of sliding
window, it is easier for us to divide guard band from occupied
band by the preset threshold:
1) Pw < Thr1 : region of guard band
2) Pw > Thr1 : region of primary user occupying band

where Pw is the power of frequency sample after sliding
window, Thr1 is the preset threshold 1. If the power is larger
than threshold 1, the sample is considered in the range of
primary user occupying band. Otherwise, we consider it the
sample in guard band. Based on this method, the guard band is
estimated and differentiated from primary signal occupying
band.
D. Primary Signal Decision
This is the last step to decide the existence of primary user
signal. Since the guard band is identified from the primary
signal occupying band, we can calculate the only noise power
in the guard band and find the power of noise part in primary
signal occupying band through (1). Then, we can estimate the
only power of primary signal by using (2). Finally, using the
preset threshold 2, the existence of primary signal is declared
to exist.
1) E > Thr2: primary user signal exist.
2) E < Thr2: primary user signal is absent.
where E is the estimated value of signal power only. If the
estimated signal power is larger than Threshold 2, the
existence of primary user is declared. Otherwise, the absence
of primary user is declared. As a result, we can detect the
existence of primary user signal by alternative energy detector.
4. Simulation Results
In order to evaluate the performance of proposed algorithm,
the simulation is performed with a pair of probabilities Pd and
Pf. Pd is the detection probability, while Pf indicates the false
alarm probability. Furthermore, the specific thresholds are
selected to meet with the requirement of Pf smaller than 10%.
The parameters including threshold 1 and 2, sliding window
size, FFT size are fully considered in the paper.
The performance of ATSC DTV signal using the proposed
algorithm is evaluated with comparison to that of conventional
energy detection in figure 7. Comparing to the conventional
one, under the same Pd 90%, the proposed method has about
7.5dB SNR gain. This is the reason that the primary signal is
easily detected by using sliding window and guard band
identification scheme.
The effect of different window sizes is evaluated to illustrate
the performance of the proposed detector as shown in figure 8.
Larger the window size, better detection performance because
the larger sliding window is more effected by averaging. There
is almost 4dB gain with window size increasing from 10 to
300. Moreover, the performance with different FFT sizes is
evaluated in figure 9. The simulation result of lager FFT size
has better performance than small FFT size. In general, in case
of large FFT, the simulation has better performance. Under the
same Pd 90%, the performance shows almost 3dB gain with
FFT size increasing from 512 and 2048.
Probablity of Detection
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
Altervative
energy detection
0.1
Conventional
energy detection
0
-20 -18 -16 -14 -12 -10 -8 -6 -4 -2 0
SNR(Eb/N0)
Figure 7. Performance for DTV Signal Detection Comparing to
Conventional one (Window Size: 300; FFT Size: 2048; AWGN)
Probablity of Detection
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
window size=300
0.2
window size=200
window size=100
0.1
window size=50
window size=10
0
-20 -15 -10 -5
SNR(Eb/N0)
Figure 8. Performance for DTV Signal Detection with Different Window
Size (FFT Size: 2048; AWGN)
Probablity of Detection
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
FFT size=2048
FFT size=1024
FFT size=512
0
-20 -15 -10 -5
SNR(Eb/N0)
Figure 9. Performance for DTV Signal Detection with Different FFT Size
(Window Size: 300; AWGN)

5. Conclusions
For spectrum sensing of cognitive radio, conventional energy
detection suffers from bad performance under low SNR
environment. Thus, we proposed the alternative energy
detector which can estimate the exact PU power without noise
effect. Furthermore, in order to appease the noise fluctuation,
the sliding window scheme is utilized to assist the detector.
The simulation result proves the better detection performance
over conventional one. In our future work, we will focus on
evaluating our proposed method under the environment with
adjacent channel interference and will introduce the more
realistic environment.
ACKNOWLEDGMENT
This work was supported by the Korea Science and
Engineering Foundation (KOSEF) through the National
Research Lab. Program funded by the Ministry of Science and
Technology (No. M10600000194-06J0000-19410) and
supported by the MIC (Ministry of Information and
Communication), Korea, under the ITRC (Information
Technology Research Center) support program supervised by
the IITA(Institute of Information Technology Assessment)"
(IITA-2006-C1090-0603-0019)
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