A combined spectrum sensing method based DCT for cognitive radio system

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 10, No. 2, April 2020, pp. 1935~1942
ISSN: 2088-8708, DOI: 10.11591/ijece.v10i2.pp1935-1942  1935
Journal homepage: http://ijece.iaescore.com/index.php/IJECE
A combined spectrum sensing method based DCT for cognitive
radio system
Muntasser S. Falih, Hikmat N. Abdullah
Department of Information and Communication Engineering, Al-Nahrain University, Iraq
Article Info ABSTRACT
Article history:
Received Jun 15, 2019
Revised Oct 23, 2019
Accepted Nov 3, 2019
In this paper a new hybrid blind spectrum sensing method is proposed.
The method is designed to enhance the detection performance of
Conventional Energy Detector (CED) through combining it with a proposed
sensing module based on Discrete Cosine Transform (DCT) coefficient’s
relationship as operation mode at low Signal to Noise Ratio (SNR) values.
In the proposed sensing module a certain factor called Average Ratio (AR)
represent the ratio of energy in DCT coefficients is utilized to identify
the presence of the Primary User (PU) signal. The simulation results show
that the proposed method improves PU detection especially at low SNR
values.
Keywords:
Cognitive radio
DCT
Hybrid sensing
Spectrum sensing
Copyright © 2020 Institute of Advanced Engineering and Science.
All rights reserved.
Corresponding Author:
Muntasser S. Falih,
Department of Information and Communication Engineering,
Al-Nahrain University,
Baghdad/Jadriyah, Iraq.
Email: mntaser.faleh.1990@ieee.org
1. INTRODUCTION
The modern requirements on wireless devices services such as smarts phones, Tablets, and new
applications that allow multimedia exchanging becomes massive and bandwidth consumer services.
This, in turn, leads to scarcity in spectrum availability. Cognitive Radio (CR) is an emerging technology
demanding to achieve Dynamic Spectrum Allocation (DSA) that aims to compensate the spectrum stinginess.
According to the Federal Communication Commission (FCC) survey revealed that the daily use of licensed
spectrum by the Primary User (PU) is lower than 30% in some geographic area [1-4]. By using CR, these
opportunities can be utilized in spectrum such as in TV band and 4G cellular networks to overcome
the spectrum problem and optimize resources usage. CR as a system consists of the life cycle the basic and
more sensitive step is spectrum sensing cycle. Spectrum sensing is the process that achieved by Secondary
User (SU) side to know the PU status which either idle or busy with a purpose to detect the spectrum
holes [5-9]. There are many narraw-band spectrum sensing methods such as Matched Filter [10],
Cyclostationary [11], Energy Detector [12], features based [13] and eigen value based [14] . Onother hand,
there aremany spectrum sensing methods specialized for wide-band channles such as compressive
sensing [15, 16] and edge based detectiom methods [17]. The main challenge of spectrum sensing is achieve
a spectrum sensing method that mixes the sufficient detection accuracy at low SNR values with acceptable
processing complexity. Solving this challenge will act to enhance the overall spectrum utilization and
increasing the total network throughput. In this paper, a hybrid energy detection method mixes
the Conventional Energy Detector (CED) with an assisting sensing module based on the Discrete Cosine
Transform (DCT) is proposed.

 ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 10, No. 2, April 2020 : 1935 - 1942
1936
2. RELATED WORKS
Many efforts are proposed in the literature to improve the detection performance of energy detection
based spectrum sensing using a statistical measure of PU and noise signals. For example, in [18] the authors
introduced a sensing method use a statistical parameter which represents the ratio between variance and mean
values of energy as an indicator whether the received signal is a PU or noise alone. In [19], the authors
proposed a spectrum sensing method based on using noise uncertainty factor to update the threshold value of
the energy detector. In [20], the authors proposed an adaptive spectrum sensing method based on higher order
moment and cumulate with a multilevel threshold for decision stage. There are another efforts to developing
CED by utilizing another transformation approaches for example In [21] the authors designed a new energy
based spectrum sensing using DCT instead of Discrete Fourier Transform (DFT), this was a good method but
not take into account the noise signal recognition from PU signal from DCT coefficient’s relationship.
According to the related works above, it can be seen that most of these efforts focused on utilizing
complex approaches to achieve high detection accuracy and did not take into account the recognition
performance of PU and noisy signals based on variation resulting from transformation of these signals.
Another most important note can be seen, is that the all above works did not utilize the traditional energy
detection method as assisting module to simplify the detection complexity when the received signal falls in
medium SNR range, which in turn can provide acceptable sensing accuracy with low overhead that gives
short detection time and increased network throughput. In this paper, a new approach to distinguish between
PU and noise only signals by utilizing DCT transform features is proposed. The distribution of energy in
DCT sub-bands, fortunately, show a wide variance between PU and noise alone signal cases especially at
lower sub-bands. This measure is used to support CED detector when it fails to produce the right decisions at
low SNR values
3. CONVENTIONAL ENERGY DETECTOR (CED)
Conventional Energy Detector (CED) is the simple context of ED method in which the Secondary
User (SU) accumulates the received energy with respect to a certain number of samples (Ns). The binary
hypothesis used in the detection criteria is as follows [22]:
( ) {
( )
( ) ( )
(1)
Where y(n) is the received sample, w(n) is the Additive White Gaussian Noise AWGN sample collected from
the transmission channel, s(n) is the PU signal sample, H0 and H1 are null and power hypothesis
respectively. The test statistic of the received sample is defined as follows:
∑| ( )| (2)
Where Ns is the total number of received samples. The distribution of T is chi-squared with 2Ns degrees of
freedom [23]. The received samples are compared with predefined threshold computed based on Constant
False Alarm probability (CPFA) [24, 25]. The performance of the detection in energy detector schemes is
evaluated based on Receiver Operation Characteristics (ROC) that showing detection probability (Pd), False
Alarm Probability (PFA), Signal to Noise Ratio (SNR), and threshold (λ). ROC parameters are computed in
AWGN as follows [26].
PD= [ | ] (
( )
√ ( )
) (3)
PFA= | (
√
) (4)
Where here Q is complementary Q-function (Marcum), Pd represents the correct detection when PU is
present, PFA represents false detection alarm that indicates the presence of PU but, actually it is not there.
PD and PFA in (3) and (4) are calculated in case of unity noise variance. From (4) λ is calculated as follows:
λ= √ ( ) (5)
where Q-1
is inverse Q-function. The system model of CED method is shown in Figure 1 [7].

Int J Elec & Comp Eng ISSN: 2088-8708 
A combined spectrum sensing method based DCT for cognitive radio system (Muntasser S. Falih)
1937
Figure 1. CED block diagram
4. THE PROPOSED METHOD
The method mixes the CED with an enhancement sensing module based on DCT statistics as shown
in Figure 2. This method has two operation modes. The first mode is performing spectrum sensing using
CED method when the received energy is greater or equal to the predefined threshold. When the received
energy is less than the predefined threshold, the second mode is initiated where enhancing module based on
DCT statistics is activated. The enhancement module will act to make further verification about
the hypothesis decision produced by CED energy detector. The verification mechanism can be explained as
follows: first DCT transform is computed to received samples using (6) and (7) [27]:
A[0]=
√
∑ , M=0 (6)
A[M]= ∑ (
( )
), 1≤ M ≤ N-1 (7)
Where a[n] and A[M] are the original and DCT N-point signals. After that, the whole DCT spectrum band
(range) of length N is divided the into K non-overlapped sub-bands of length N/K as shown in Figure 3 such
that the ith sub-band has local energy Ei (i=1,2,…. K). The energy of first and last sub- bands (E1, EK) are
calculated using (8) and (9) then Average Ratio (AR) between E1 and EK is calculated using (10).
E1= ∑ | ( )| (8)
EK= ∑ | ( )| . (9)
AR= (10)
Figure 2. The proposed algorithm flowchart

 ISSN: 2088-8708
1938
Figure 3. DCT’s Sub-bands division
Based on the value of AR, the signal type can be identified which either PU (PU+Noise) or noise
only signal. In the case of PU presence, the AR value will be greater than one depending on the SNR value of
the transmitted signal. This is related to DCT feature that the lower bands contain the most of signal energy
(information), in opposition to the higher bands that contain less energy. In the case of noise-only signal
the AR will be approximately unity because there is no source of information except the noise and each
sub-band approximately collects the same amount of energy. Figure 4 and Figure 5 show the testing of AR
factor in two scenarios, the first scenario assumes PU is present and the second assumes PU is absent with
SNR as a parameter respectively.
Figure 4. AR factor testing for PU and noise signals
at SNR=-8dB
Figure 5. AR factor testing for PU and noise signals
at SNR=8dB
From Figure 4 and Figure 5 it can be seen that the value of AR provides an excellent measure to
the presence or absence of the PU signal. The rounding of AR will be to unity in case of noise only signal
and greater than one in case of PU presence; meanwhile, it can be used as a new threshold for the use at
the detection in CED assisting module. The detection mechanism is achieved by comparing the value AR
with value of “1”, if the value of AR less or equal to 1 the hypothesis is H0; otherwise it is H1. The flowchart
of the CED assisting sensing module is shown in Figure 6. The use of this two- mode sensing scheme will
provide a good compromise between low complexity provided by CED at high SNR values and accuracy
provided by DCT at low SNR values.
The detection probability of the proposed method (PDPRO) is a composite detection probability
includes detection probability of CED and the DCT based assisting module as in (11).
PDPRO=PINCED*PDCED+(1-PINCED)*PDDCT (11)
where PINCED is the probability of falling in CED sensing event, PDCED is the detection probability of the CED
method, and PDDCT is the detection probability of DCT based assisting module.

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Figure 6. Flowchart of CED assisting sensing module based on DCT
5. RESULTS
The proposed method is simulated using MATLAB. The evaluation has been tested for Local Sensing
(LS) noncooperative scenario showing the ROC with different values of NS, PFA and channels types.
The simulation parameters used are given in Table 1. Figure 7 and Figure 8 show the performance of
the proposed method versus CED in AWGN channel at NS= 1000, PFA= 0.001 and PFA =0.01 respectively.
It can be seen from these figures that the proposed method significantly improve the detection probability
compared to CED method and this improvement increases as PFA decreases. For instance, at SNR=-9 dB,
the detection probability is improved by 95% and 90% using the proposed method when PFA is 0.001 and
0.001 respectively. Also, we notice from the performance curves that the detection probability of the proposed
method reaches its optimum value of one in about 4 dB SNR earlier than that of CED method.
Figure 9 shows the performance of proposed and CED methods in Rayleigh fading channel at
NS=1000 samples and PFA=0.001. Figure 10 shows the performance of the proposed method at a different
number of received samples. From Figure 9 it can be seen the proposed method achieves gain of 20%
improvement in detection energy at SNR=-11 dB, while in Figure 10 shows the flexibility of the proposed
method to operate in different number of received sample with almost similar performance especially as
Ns=500 and Ns=250 sample.
Figure 11 shows the performance of the proposed method compared with the method in [4].
From this figure, it can be seen that our proposed method outperform the method of [4] in detection
probabilty. For instance, at SNR=-10 dB, Pd is incresed from 0.5 to 0.8 by improvement factor of 60%.
Also, we notice that the detection probability of the proposed method reaches its optimum value of one in
about 3.5 dB SNR earlier than that of the method in [4]. Finally, the complexity of the proposed method
evaluated using sensing time as depicated in Figure 12. This figure shows the Simulation time comparison
between proposed method and CED. From this figure it can be seen that proposed method time consumer
than CED at low SNR values when it operate DCT based sensing module, then it return to normal state when
CED is operating at high SNR values.
Table 1. Simulation Parameters
Parameter Value
PU signal Type QPSK
Channel Type AWGN and Rayleigh fading
PFA 10-2
and 10-3
Number of Samples Ns 1000, 500 and 250
SNR Range -15 to 0 dB

 ISSN: 2088-8708
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Figure 7. Performance of the proposed method with
CED at PFA= 0.001 and NS=1000
Figure 8. Performance of the proposed method with
CED at PFA= 0.01 and NS=1000Performance of
the proposed method with CED at PFA= 0.001 and
NS=1000
Figure 9. Performance of the proposed method and
CED in Rayleigh fading channel at PFA= 0.001 and
NS=100
Figure 10. Performance of the proposed method at
different NS in AWGN
Figure 11. Performance comparison of the proposed
method with method in [4]
Figure 12. Complexity performance evaluation

1941
6. CONCLUSION
In this paper, a hybrid blind spectrum sensing method has been presented. This method mixes CED
with sensing assisting module based on DCT. This method operates in CED mode when received signal
energy equal or greater than the threshold value, and operates the assisting sensing module mode when
the energy falls below the threshold value. The result showed that the proposed method over perform CED in
both types of tested channels, for an instant in AWGN the detection probability enhanced by 95% at SNR
of -9dB. As well as in fading channel detection probability has been enhanced by 90% at SNR of -9 dB.
According to the provided advantages the proposed spectrum sensing method, it represents a suitable method
for sensing a low SNR for different signal type without synchronization due to it is blindness nature with
energy saving facility that resulted from the switching between CED and the proposed sensing module with
respect to the received signal strength.
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BIOGRAPHIES OF AUTHORS
Muntasser S. Falih was born in Al-Imara, Iraq in 1990. He obtained his B.Sc. in Information
and Communication Engineering in 2013 at University of Baghdad, Iraq, M.Sc. in Networking
Engineering in 2016 at Al-Nahrain University, Iraq and he is now a Ph.D. student in Information
and Communication Engineering at Al-Nahrain University, Iraq. He is work on the project of
developing the cognitive radio sensing algorithms as Ph.D. research. He interested in subject of
wireless communications, security and cognitive radio networks.
Hikmat. N. Abdullah was born in Baghdad, Iraq in 1974. He obtained his B.Sc. in Electrical
Engineering in 1995, M.Sc. in Communication Engineering in 1998 at Al-Mustansiryah
University, Iraq and Ph.D. in Communication Engineering in 2004 at University of Technology,
Iraq. From 1998 to 2015 he worked as lecturer/associate professor in the Electrical Engineering
Department, at Al-Mustansiryah University, Iraq. Since the beginning of 2015 he works as
associate professor/ full professor in college of Information Engineering at Al-Nahrain
University, Iraq. From 2011–2013 he got a research award from International Institute of
Education (IIE/USA) at Bonn-Rhein-Sieg university of applied sciences, Germany. He is asenior
member of IEEE association since 2014. He is interested in subject of wireless communications,
chaotic communications and cognitive radio networks.

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