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ICCPCT.2017.8074308

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2017 International Conference on circuits Power and Computing Technologies [ICCPCT]
Prominence of Cooperative Communication in 5G
Cognitive Radio Systems
G. Shine Let
G. Josemin Bala,
J. Jenkin Winston
M. Deepak Raj
C. Benin Pratap
ECE-Department of
Electrical Technology,
Karunya University,
Coimbatore, India.
shinelet@gmail.com
ECE-Department of
Electrical Technology,
Karunya University,
Coimbatore, India.
ECE-Department of
Electrical Technology,
Karunya University,
Coimbatore, India.
ECE-Department of
Electrical Technology,
Karunya University,
Coimbatore, India.
EEE-Department of
Electrical Technology,
Karunya University,
Coimbatore, India.
Abstract— Cognitive Radio is a promising way to overcome
the spectrum scarcity for wireless communication and improving
the spectral efficiency by using the vacant licensed spectrum
band. Cooperative communication is a new communication
technique which utilizes the help of neighboring nodes to reduce
the bit error rate (BER) in a harmful fading environment. The
challenging factor is to combine the cooperative communication
in cognitive radio to improve the spectral efficiency and to reduce
the BER factor of unlicensed user’s communication. In this
paper, a comparative study of different communication
techniques are done by considering Rayleigh fading channel
environment and the advantages of cooperative communication is
analyzed. Also, the paper deals with the challenges of integrating
cooperative communication in cognitive radio network are
discussed.
Keywords—Cognitive Radio, Cooperative Communication,
Decode-and-Forward protocol
I. INTRODUCTION
To efficiently utilize the frequency spectrum and to
moderate the probability of error in new generation mobile
communication systems new techniques are considered with
the existing techniques. To resourcefully use the licensed
spectrum, cognitive radio concept was introduced by Simon
Haykin in [1]. To moderate the probability of error, cooperative
communication was introduced by [2] for wireless network.
The cognitive radio and cooperative communication concept
can be combined for 4G and 5G mobile communication
system. In cognitive radio networking, the mobile devices
equipped with cognitive radio will able to access the licensed
spectrum when the licensed user is not accessing the spectrum.
Research on cognitive radio started due to the wastage of
spectrum in allotted TV band. Based on the spectrum
occupancy survey taken in New York City approximately 80%
of the spectrum is not utilized [3-4].
As day goes by, the number of mobile users increases
tremendously. To minimize the probability of Bit-Error-Rate
(BER) different diversity techniques such as time-diversity,
frequency diversity, spatial diversity etc. are considered in the
communication scenario. Out of many diversity techniques
spatial diversity technique effectively addressed the
performance degradation due to multipath fading. This
diversity technique does not consider any time delay or
bandwidth expansion for improving system efficiency. In
mobile communication, employing spatial diversity in base
station seems easier. But in mobile stations, including more
978-1- 5090-4967- 7/17/$31.00 © 2017 IEE
number of antennas is not possible because of its size and
hardware complexity. Having more number of antennas in
transmitter and receiver is called as MIMO (Multiple-Input
Multiple-Output). Implementing MIMO concept in mobile
finds it difficult by the researchers. To overcome this difficulty,
Cooperative Communication was emerged and it has the
benefits of MIMO. Main advantage in cooperative
communication is many mobiles with single antenna will share
their antenna to efficient communication. Thus, the cooperative
communication is also called as “virtual-MIMO” [2].
In 4G and 5G mobile communications, cooperative
communication plays a vital role for the improvement of
mobile user’s communication in a fading environment. The
different ways of cooperative communication and its
performance is shown in the following sessions.
II. IMPORTANCE OF COOPERATIVE COMMUNICATION
For cooperative communication, neighboring nodes will
help the sender to forward the data to the destination.
Neighboring nodes are also called as relay nodes. The relay
node will perform amplify-and-forward, decode-and-forward
or compress-and-forward.
In Amplify-and-forward (AF) protocol [5], the relay node
amplifies the received data and forwards to the destination.
Due to wireless scenario, the data received by relay node is
affected with noise. The data along with noise is amplified and
forwarded to destination using AF protocol. This is the major
drawback of AF protocol. In Decode-and-forward (DF)
protocol [6], the data received is decoded by the relay node. If
the data is decoded correctly, the information is forwarded to
the destination. For dynamic traffic and channel conditions, DF
protocol performs well compared to AF protocol. Also, DF
protocol is simple to implement.
In Compress-and-forward (CF) protocol [7], the relay node
compresses the received data and encodes it into a new data
and forward to destination. This protocol increases security and
capacity in the transmission link.
A. BER performance in Direct Communication
Now-a-days in mobile communication, the sender
transmits the data to base-station in wireless medium. From
base-station (BS) the data is forwarded to Mobile Switching
Centre
(MSC)
and
this
connection
is
wired.
2017 International Conference on circuits Power and Computing Technologies [ICCPCT]
Here for simulation, QPSK modulated signal is transmitted
from source to destination wirelessly. Rayleigh fading channel
is considered. Fig 1 shows the direct communication scenario.
Fig:3 SIMO Communication
Fig 1: Direct Communication Scenario
The received signal at the destination is given by,
ysd = √P hsdx + wsd (1)
where ‘P’ is the transmit power at the source, ‘hsd’ is the
source destination channel coefficient which have Rayleigh
distribution, ‘x’ is the transmitted symbol (either ‘-1’ or ‘1’),
‘wsd’is the Gaussian noise. The probability density function of
Rayleigh fading environment is given by
(2)
where ‘a’ is the envelope of fading channel and ‘δ2’ is the
average power of the fading channel.
Fig 2 shows the probability of symbol error rate (SER) vs
signal-to-noise (SNR) ratio for direct communication.
The received signal at the destination by antenna-1 is given
by,
y1 = √P h1x + wsd1 (3)
The received signal at the destination by antenna-1 is given
by,
y2 = √P h2x + wsd2 (4)
where, ‘h1’& ‘h2’are the channel coefficient which have
Rayleigh distribution between source – destination antenna-1
and source – destination antenna-2 respectively, ‘wsd1’ &
‘wsd2’are the Gaussian noises in anteena-1 and antenna-2
respectively.
Fig: 4 shows the probability of symbol error rate (SER) vs
signal-to-noise (SNR) ratio for SIMO communication.
0
10
SIMO Communication
-1
10
0
10
SER
Direct Communication
-1
-2
10
SER
10
-3
10
-2
10
-4
10
0
2
4
6
8
10
12
SNR in dB
14
16
18
20
-3
10
0
2
4
6
8
10
12
SNR in dB
14
16
18
20
Fig:2 SER vs SNR (dB) in Direct Communication
B. BER performance in SIMO Communication
SIMO communication means Single-Input Multiple-Output
communication where single antenna is used in transmitter side
and multiple antennas are used in receiver. By using multiple
antennas in the transmitter and receiver side, the fading effect
due to wireless environment can be reduced and thus the biterror rate of the received signal can be reduced. For simulation,
two antennas at the receiver are considered.
Compared to the direct communication the bit-error rate is
reduced. But, practically having multiple antennas in mobile is
difficult. Now-a-days mobile device thickness is very small. So
integrating many antennas within small area is difficult. If
multiple antennas are integrated in one device the selfinterference will be more. Fig: 3 shows the simple SIMO
communication system considered for simulation.
Fig:4 SER vs SNR (dB) in SIMO Communication
C. BER performance in Cooperative Communication using AF
protocol
In cooperative communication, the source information is
forwarded by intermediate nodes apart from direct
communication. Here the relay node amplifies the received
message and then forwards to destination. Fig 5 shows the
cooperative communication using one relay node.
Fig 5: Cooperative Communication using Relay node
2017 International Conference on circuits Power and Computing Technologies [ICCPCT]
In Amplify-and-Forward based cooperative system, during
phase-1 the source transmits the information to destination and
relay. In phase-2, relay amplifies the received signal and
forwards it to the destination.
The received signal at the destination and relay at phase-1
is given by,
ysd = √P hsdx + wsd
(5)
(6)
ysr = √P hsrx + wsr
ysd = √P hsdx + wsd
ysr = √P hsrx + wsr
In phase-2, the relays decode the received signal. The relay
which decoded the data correctly re-encodes and forwards to
the destination. In destination, maximum ratio combiner is used
to combine the received signal from source and relays. The
received signal at the destination in phase-2 is given by
equation (10)
yrd = √P1 hrdx+ wrd
The received signal at the destination at phase-2 is given by,
yrd = √P1 hrd
1
2
P | h sr | + N 0
+ wrd (7)
where, P and P1 are the transmit power at the source and
relay respectively. Fig: 6 shows the probability of symbol
error rate (SER) vs signal-to-noise (SNR) ratio for AF
protocol cooperative communication.
(8)
(9)
(10)
Fig 8 shows the probability of symbol error rate (SER) vs
signal-to-noise (SNR) ratio using selective DF protocol in
cooperative communication.
0
10
Selective DF Protocol
-1
10
0
10
AmplifyForward Communication
-2
10
-1
SER
10
-3
SER
10
-2
10
-4
10
-3
10
-5
10
-4
10
0
2
4
6
8
10
12
SNR in dB
14
16
18
20
Fig 6: SER vs SNR (dB) in Cooperative Communication using
AF protocol
D. BER performance in Cooperative Communication using DF
protocol
Consider multiple relays are used for cooperative
communication as shown in fig 7. Here each relay uses decodeand-forward protocol. The relay which decodes the received
data correctly, re-encodes the data and forward to destination
[8].
Fig 7: Cooperative Communication using multiple relay nodes
In phase-1, source broadcasts its information to the
destination and relays. The received signal at the destination
and relays is given by equation (8) and (9)
0
2
4
6
8
10
12
SNR in dB
14
16
18
20
Fig 8: SER vs SNR (dB) in Cooperative Communication using
Selective-DF protocol
III. COMPARATIVE STUDY
The performance of different protocol is analyzed by
transmitting the data through Rayleigh fading channel.
TABLE I.
COPERATIVE COMMUNICATION COMPARITIVE STUDY
Communication Types
Symbol Error
Rate(SER) for SNR
20dB
Direct Communication
9.48 x 10-3
Single-Input Multiple-Output
(SIMO) communication
Cooperative Communication using
AF protocol
Cooperative Communication using
Selective-DF protocol
2 x 10-4
8 x 10-4
9 x 10-5
Table-1 shows the comparison of different ways of
communication with respect to symbol error rate (SER). From
the tabulation, it has been same the SER due to SIMO
communication and cooperative communication using
selective-DF protocol is almost same. In SIMO
communication, multiple antennas have to be integrated in one
device. Placing the antenna properly in the device by
minimizing self-interference is difficult. But, the same SER is
achieved by having cooperation between intermediate devices.
2017 International Conference on circuits Power and Computing Technologies [ICCPCT]
IV. COOPERATIVE COMMUNICATION IN COGNITIVE RADIO
NETWORK
Spectrum sensing is the main challenge in cognitive radio
network. The unlicensed users are also called as secondary
users have to first find the vacant spectrum before
communication. To find the vacant spectrum with less
probability of error, neighboring nodes information is
considered. Thus cooperative communication techniques are
used in cognitive radio spectrum sensing. By using cooperative
sensing, false alarm probability and hidden terminal problem
can be reduced and signal detection accuracy can be improved.
Cooperative spectrum sensing is classified as centralized,
distributed and relay-assisted spectrum sensing and various
parameters are analyzed in different techniques. In [9], sensing
diversity gain is analyzed by using cooperative spectrum
sensing in cognitive radio network. In [10], paper deals with
different decision fusion rule in cooperative spectrum sensing.
The performance of OR rule gives better results in many
practical scenarios.
The different challenges in cooperative spectrum sensing
are to have control channel for sensing, increase in sensing
time, and energy considerations. Apart from cooperative
sensing, cooperative data transmission can also be done in
cognitive radio network among unlicensed users. In [11], a
half-slotted ALOHA multiple access protocol is used for the
data transmission. Time slot is allotted for primary and
secondary user communication. In this paper [11], secondary
user transport capacity is analyzed according to the coverage
radius.
carried out in cognitive radio network, to have high quality-ofservice for secondary users’ communication.
V. CONCLUSION
Cooperative communication in cognitive radio network
seems to be more prominence in 5G wireless communication.
This overcomes the effect of under spectrum utilization, fading
effect, path loss, shadowing etc. This paper gives the
comparison of different cooperative communication techniques
simulated in Rayleigh fading environment. Selective Decodeand-forward protocol gives 9x10-5 symbol error rate compared
to other protocols. Also, the paper gives some insights on
challenges in having cooperative communication in cognitive
radio network as a future work.
REFERENCES
[1]
[2]
[3]
[4]
[5]
A new algorithm was proposed in [12] by combining DF
and AF protocol and it is named as Hybrid-Decode-AmplifyForward (HDAF). Outage probability is analyzed by
considering different interference constraint. The best relay
performance is selected to reduce the interference effect from
primary users. The outage probability can be reduced by
considering more number of potential relay. The performance
of cognitive radio network has been analyzed by considering
multiple communications between secondary users as future
work in [12].
[6]
A cooperative communication is done between devices
under a base station using selective-relay protocol. To deal
with relay selection and efficient resource sharing, bar-gaining
game is introduced in [13]. The system efficiency and fairness
are analyzed. This approach can be used for efficient secondary
user’s communication in cognitive radio network. In [14],
outage performance of cognitive radio system is demonstrated
by considering cooperative relay system and MIMO system.
Compared to SISO, the performance of the system gives better
outage.
[10]
Having multiple antennas in a device is practically difficult.
Cooperative communication among multiple devices provides
better outage performance in cognitive radio secondary user
communication. By ensuring cooperative communication in
cognitive radio network, the spectrum utilization can be
improved. Also, the degradation of performance due to fading
environment can be avoided. More research work can be
[7]
[8]
[9]
[11]
[12]
[13]
[14]
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