Performance Evaluation of Energy Detector Based Spectrum Sensing for Cognitive Radio using NI USRP-2930

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 7, No. 4, August 2017, pp. 1934 – 1940
ISSN: 2088-8708 1934
Institute of Advanced Engineering and Science
w w w . i a e s j o u r n a l . c o m
Performance Evaluation of Energy Detector Based Spectrum
Sensing for Cognitive Radio using NI USRP-2930
F. Z. El Bahi, H. Ghennioui, and M. Zouak
Laboratoire Signaux, Systèmes et Composants (LSSC), Faculté des Sciences et Techniques de Fès (FSTF)
Université Sidi Mohamed Ben Abdellah (USMBA), Route Immouzzer, B.P. 2202, Fès-Maroc
Article Info
Article history:
Received: Feb 18, 2017
Revised: May 28, 2017
Accepted: Jun 12, 2017
Keyword:
Energy Detector
Spectrum Sensing
Cognitive Radio
Primary User
Secondary User
USRP
MATLAB
ABSTRACT
This paper presents the performance evaluation of the Energy Detector technique, which is
one of the most popular Spectrum Sensing (SS) technique for Cognitive Radio (CR). SS
is the ability to detect the presence of a Primary User (PU) (i.e. licensed user) in order to
allow a Secondary User (SU) (i.e unlicensed user) to access PU’s frequency band using CR,
so that the available frequency bands can be used efficiently. We used for implementation
an Universal Software Radio Peripheral (USRP), which is the most used Software Defined
Radio (SDR) device for research in wireless communications. Experimental measurements
show that the Energy Detector can obtain good performances in low Signal to Noise Ratio
(SNR) values. Furthermore, computer simulations using MATLAB are closer to those of
USRP measurements.
Copyright c 2017 Institute of Advanced Engineering and Science.
All rights reserved.
Corresponding Author:
Fatima Zahra EL BAHI,
Laboratoire Signaux, Systèmes et Composants (LSSC), Faculté des Sciences et Techniques de Fès (FSTF),
Université Sidi Mohamed Ben Abdellah (USMBA), Route Immouzzer, B.P. 2202, Fès-Maroc.
Email: fatimazahra.elbahi@usmba.ac.ma
1. INTRODUCTION
In recent years, the wireless applications and devices have developed and increased rapidly. Since the access
to electromagnetic spectrum is fixed and limited, thus the available frequency bands are inefficiently utilized which
causes the spectrum scarcity problem [1]. In order to use the available spectrum efficiently, many studies and re-
searches have proposed a new concept called Cognitive Radio (CR). This concept is based on the opportunistic usage
of radio frequency bands by allowing Secondary Users (SUs, i.e. unlicensed users) to exploit frequency bands of
Primary Users (PUs, i.e. licensed users). CR is a radio for wireless communications able to change its parameters,
related to either transmission or reception, autonomously and dynamically based on the electromagnetic environment
and communication requirements, in order to perform an efficient communication without interfering with PUs [2].
CR has the capability of sensing the spectrum, thus detect the presence PU’s signal, using different spectrum
sensing techniques. These techniques are subdivided into two categories: cooperative and non-cooperative sensing
techniques. The first category is based on sharing information, in other words, the detection of Primary User’s signal is
performed by combining results from multiple cognitive radios that works together [3]. The most important advantage
of this technique is the capability of decreasing sensing time and improving the sensing accuracy. The second category,
non-cooperative sensing techniques, is also known as primary transmitter detection, because the detection of PU’s
signal is based only on the received signal at a SU. The most common sensing techniques that belong to this category
are: energy detector [4], matched filter detector and cyclostationary detector [5]. Energy detector is the most popular
spectrum sensing technique, it can also be considered as the most spectrum sensing technique used in practice because
of its low implementation complexity. The concept of Energy detector is based only on computing the total energy
of the received signal, then comparing is to a specified threshold in order to decide the presence or absence of a PU’s
signal, thus no prior knowledge of the PU’s signal is required, only the variance of the noise is needed.
In this paper, we present an implementation of the Energy Detector Based Spectrum Sensing, using an Univer-
sal Software Radio Peripheral (USRP), a Software Defined Radio (SDR) device, in order to evaluate the performance
Journal Homepage: http://iaesjournal.com/online/index.php/IJECE
Institute of Advanced Engineering and Science
w w w . i a e s j o u r n a l . c o m
, DOI: 10.11591/ijece.v7i4.pp1934-1940

IJECE ISSN: 2088-8708 1935
of the spectrum sensing technique. We used for the implementation an NI USRP-2930, which belongs to the USRP se-
ries of the National Instruments’ brand. For the programming, we used LabVIEW and MATLAB for both transmission
and reception. We implemented for transmission an OFDM (Orthogonal Frequency Division Multiplexing) Signal,
which is used as a Primary User’s signal. We chooses the OFDM modulation because it is the most used in wireless
communication, due to its high bandwidth efficiency [6], such as WiFi [7], 3GPP/LTE [8] (3rd Generation Partnership
Project/Long Term Evolution) for the downlink, DVBT [9] (Digital Video Broadcast Terrestrial) and WiMAX [10]
(Worldwide Interoperability for Microwave Access). The performance evaluation of the Energy Detector is based on
experimental measurements obtained using USRP’s receiver, which included the Energy Detector algorithm.
The rest of this paper is organized as follows. In section 2, we introduce the Energy Detector based sensing
after defining the problem formulation. Section 3 defines the implementation details using NI USRP-2930. Finally, in
section 4, the experimental results obtained from the implementation are provided in order to illustrate the performance
of Energy Detector.
2. ENERGY DETECTOR BASED SENSING
2.1. Problem Formulation
In our system model, a SU senses the presence of a PU. In order to perform a good detection of a spectrum
opportunity, two hypotheses, H0 and H1 are defined respectively for the absence and the presence of a PU signal.
Hence, our hypothesis model for transmitter detection can be expressed as follows:
H0 : s(l) = n(l)
H1 : s(l) = x(l) + n(l)
, (1)
where, s(l) represents the received data, x(l) is the transmitted signal by the primary user and n(l) denotes the White
Gaussian noise independent from the transmitted signal, with zero mean and variance σ2
n. From the two hypotheses,
two probabilities describe the performance of the spectrum sensing technique: the false-alarm probability Pfa, which
is the probability of declaring wrongly H1 and the detection probability Pd, which is the probability of declaring
correctly H1. The main purpose of all spectrum sensing techniques is to maximize the detection probability for a low
false-alarm probability.
2.2. Energy Detector
Energy Detector, also known as radiometry, is the most popular and widely used spectrum sensing technique
because of its low computational and implementation complexities. It is a simple sensing technique that does not need
prior knowledge of the PU’s signal, only the value of the White Gaussian Noise is needed. Urkowitz [4] was the first
to investigate the detection of an unknown signal in a White noise channel using the energy detector based sensing.
The PU signal is detected by comparing the total energy of the received signal, over a specified time duration, with a
threshold. Thus, the test statistics of the energy detector is written as:
TED =
1
L
L
l=0
|s(l)|2
, (2)
where, L denotes the size of the observation sequence.
The presence of a PU’s signal is thus detected if the energy is greater than the threshold. The decision is then expressed
as follows:
TED
H1
H0
λ, (3)
where, λ denotes the threshold.
The Probability Density Function (PDF) of the test statistics TED can be modeled as a Gaussian distribution
[11] according to the two hypothesis as follows:



H0 : TED ∼ N σ2
n,
σ4
n
L
H1 : TED ∼ N σ2
x + σ2
n,
(σ2
x+σ2
n)
2
L
, (4)
Performance Evaluation of Energy Detector using NI USRP-2930 (F. Z. El Bahi)

1936 ISSN: 2088-8708
where, σ2
n represents the variance of the White Gaussian noise and σ2
x denotes the variance of the transmitted PU’s
signal.
Based on the PDF of the test statistics, the detection probability Pd and the false-alarm probability Pfa can
be expressed as:
Pfa = Pr(TED > λ|H0) = Q
λ
σ2
n
− 1
√
L (5)
Pd = Pr(TED > λ|H1) = Q
λ
σ2
n
− ρ − 1
L
2ρ + 1
, (6)
where, ρ =
σ2
x
σ2
n
denotes the Signal to Noise Ratio (SNR) and Q(.) is the Marcum Q-function deﬁned as: Q(y) =
1√
2π
∞
y
e− u2
2 du. For a target false-alarm probability, the value of the threshold λ can be calculated by inverting the
relation described in Eq. 6 as follows:
λ = σ2
n
Q−1
(Pfa)
√
L
+ 1 , (7)
where, Q−1
(.) is the inverse Marcum Q-function.
3. IMPLEMENTATION DETAILS
In this section, we provide the practical implementation details of the Energy Detector based sensing. We
used for both transmission and reception an NI USRP-2930 (Universal Software Radio Peripheral) and a Desktop
Computer with LabVIEW 2014 and MATLAB R2013a to control the USRP using a Gigabit Ethernet Cable as shown
in Figure 1.
Figure 1. Implementation’s structure
3.1. NI USRP-2930
NI USRP-2930 is a Software Deﬁned Radio (SDR) transceiver, able to transmit and receive RF (Radio Fre-
quency) signals, from the USRP series of the National Instruments’ brand, it is widely used for both teaching and
research in wireless communications. Furthermore, it enables a wide range of RF applications covering common
standards such as GSM Cellular, broadcast radio, WiFi, GPS and digital TV. The USRP hardware is a straightforward
RF platform for rapid prototyping applications such as spectrum monitoring and physical layer communication. It
has the ability to transmit and receive RF signals across a frequency range from 50 MHz to 2.2 GHz. Moreover, the
NI USRP-2930 has an integrated GPS-disciplined clock that provides GPS position information, improved frequency
accuracy and synchronization capabilities [12].
3.2. Transmitter
The programming and design parts of the transmitter are implemented in LabVIEW in order to control the NI
USRP-2930. For our experiment, we consider an OFDM (Orthogonal Frequency Division Multiplexing) transmitted
signal with a carrier frequency set to 200 Mhz.
As seen in Figure 2, the transmitter’s front panel is divided into two parts. The left part contains three
blocks; USRP Parameters, OFDM signal parameters and Debug (Figure 3). In the OFDM signal parameters block,
we can choose the OFDM standard to transmit among the following ones: 3GPP/LTE, WiMax 802.16, DVBT-2K and
802.22-1K. The right part of the transmitter represents the PSD (Power Spectrum Density) of the OFDM transmitted
signal.
IJECE Vol. 7, No. 4, August 2017: 1934 – 1940

IJECE ISSN: 2088-8708 1937
Figure 2. Transmitter’s front panel.
Figure 3. Transmitter’s tab control.
3.3. Receiver
In the same way as the transmitter, we have implemented the receiver in LabVIEW as shown in Figure 4. The
receiver’s front panel is also divided into two parts, the left one contains three blocks; the USRP Parameters, Energy
Detector and Debug. The receiver’s debug part is the same as the one of the transmitter. In the Energy Detector part,
as shown in Figure 5, we calculate the Pd (Detection Probability) for a speciﬁc value of Pfa (False-alarm Probability)
and realizations. The right part of the receiver contains the PSD of the received OFDM signal, which is almost the
same as the one of the OFDM transmitted signal.
Figure 4. Receiver’s front panel.

1938 ISSN: 2088-8708
Figure 5. Receiver’s tab control.
4. RESULTS AND DISCUSSION
In this section, we present experimental results of the implementation of the Energy Detector based sensing
using NI USRP-2930 and averaged over 1000 realizations.
4.1. Effect of SNR
In this experiment, we test the impact of different SNR (Signal to Noise Ratio) values on the Detection
Probability (Pd). We fix the False-alarm Probability (Pfa) to 0.01 and vary the SNR value from -24 dB to 0 dB with a
step of 2 dB. Each measurement result is the average value of 1000 measurement results for the same SNR value. We
generated an OFDM signal with 64 subcarriers, 10 symbols and cyclic prefix equals to 8.
Figure 6a and 6b show the MATLAB simulation (Computer simulations) and measurement results (USRP
implementation) of the Pd versus SNR of a 3GPP/LTE signal and DVBT-2K signal respectively. We can notice from
both figures that the Pd increases with the SNR values. Thus, the larger SNR, the better the detection of the OFDM
PU. Furthermore, USRP implementations are closer to MATLAB simulations.
(a)
(b)
Figure 6. Detection probability (Pd) vs. Signal to Noise Ratio (SNR) (with Pfa=0.01) of (a) 3GPP/LTE signal and (b)
DVBT-2K signal.

IJECE ISSN: 2088-8708 1939
4.2. Receiver Operation Characteristic
In order to examine the effect of Pfa on the detection performance of the Energy Detector, we fix the value
of the SNR to -10.4 dB and vary the Pfa from 0.1 to 1 with a step of 0.1. The achieved Pd as a function of Pfa is
called the ROC (Receiver Operating Characteristic) curve. With the same way as the above experiment, each result is
averaged over 1000 measurements.
Figure 7a and 7b represent the MATLAB simulation and USRP implementation of the Pd versus Pfa of a
3GPP/LTE signal and DVBT-2K signal respectively. As shown in those figures, USRP implementations are closer to
MATLAB simulations and the Energy Detector achieves good performance for all Pfa values.
(a)
(b)
Figure 7. Detection probability (Pd) vs. False-alarm probability (Pfa) (with SNR=-10.4 dB) of (a) 3GPP/LTE signal
and (b) DVBT-2K signal.
5. CONCLUSION
In this paper, we have provided an experimental performance evaluation of the Energy Detector based sensing
using NI USRP-2930, which is a Software Defined Radio (SDR) transceiver. We have tested the impact of SNR on the
detection probability. Furthermore, a ROC curve was obtained for a low SNR value. Experimental results shows that
the Energy Detector achieves good performances for low SNR values and for all Pfa values and are closer to those of
computer simulations using MATLAB.
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[4] H. Urkowitz, ”Energy detection of unknown deterministic signals,” Proc. IEEE, vol. 55, no. 4, pp. 523-531, 1967.

1940 ISSN: 2088-8708
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BIOGRAPHIES OF AUTHORS
Fatima Zahra EL BAHI is an engineer in Network and Telecommunication, graduated in 2014
from National School of Applied Sciences of Tangier, Morocco. She is currently working toward
Ph.D. degree in the laboratory of Signal, Systems and Components, in Faculty of Sciences and
Technologies of Fez at Sidi Mohammed Ben Abdelah University, Morocco. Her main research
interests are signal processing and cognitive radio.
Hicham GHENNIOUI is an assistant director of Signals, Systems and Components Laboratory at
Faculty of Sciences and Technologies, Fez, Morocco. Since 2011, he is a full-time Associate Pro-
fessor at the Faculty of Sciences and Technologies, Fez. He received the Ph.D degree in Computer
Science and Telecommunications in 2008, jointly from Mohamed V University and the Toulon Uni-
versity. In 2004, received the D.E.S.A. degree in Computer Science and Telecommunications from
the Mohamed V University. His main research interests are signal/image processing including blind
sources separation, data analytic, deblurring and cognitive radio.
Mohcine ZOUAK is the director of the Cancer Research Institute of Fez, Morocco. President of
the Conference of Deans of Faculties Scientific Morocco (CCMS). Vice President of International
Conference of Heads of Universities and Scientific Institutions of French expression CIRUISEF.
Since 2005, he is a professor of Higher Education at Faculty of Sciences and Technologies, Fez,
Morocco. He assured lessons in the areas of signal processing, electronic systems and telecommu-
nications as well as in the fields of stochastic estimation. His research activities mainly concern the
signal processing and telecommunications.

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