Minimal Throughput Maximization for MIMO Cognitive Radio Networks Using Particle Swarm Optimization

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DOI: 10.1109/MoWNet.2016.7496605 ·
Conference: 2016 International Conference on Selected Topics in Mobile & Wireless Networking (MoWNeT)
Cite this publication
Abstract
Due to the scarcity of the available spectrum bands, Cognitive Radio (CR) has been proposed as a promising paradigm. One of the most important research area is how to maximize Secondary User (SU) system throughput while assuring the Quality of service of Primary User (PU) system. Multiple-Input-Multiple-Output (MIMO) is an efficient technique, exploiting space diversity, used for increasing SUs throughput. The contribution is to maximize the minimal throughput among SUs, in particular the uplink of the infrastructure-based Cognitive Radio Networks (CRNs), through proposing a power allocation technique based on Particle Swarm Optimization (PSO) for MIMO systems in CR networks. Maximum-Minimum Throughput-based Power Assignment (MMTPA) and Equal Power Assignment (EPA), using the average minimal throughput among all SUs and also the complexity to measure performance, approaches are used for comparison. The simulation results show that the proposed approach outperforms the two other schemes, in addition, an increase in the total throughput of SU system.
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Minimal Throughput Maximization for
MIMO Cognitive Radio Networks Using
Particle Swarm Optimization
1
Abd Elhamed M. Dawoud,
1
Mona Shokair,
1
Mohamed Elkordy and
1
Said El Halafawy.
Faculty of Electronic Engineering, El-Menofia University
Menouf 32952, Egypt
Corresponding author’s email
:
abd.elhamed@el-eng.menofia.edu.eg
Abstract—Due to the scarcity of the available
spectrum bands, Cognitive Radio (CR) has been
proposed as a promising paradigm. One of the most
important research area is how to maximize
Secondary User (SU) system throughput while
assuring the Quality of service of Primary User (PU)
system. Multiple-Input-Multiple-Output (MIMO) is
an efficient technique, exploiting space diversity,
used for increasing SUs throughput. The
contribution is to maximize the minimal throughput
among SUs, in particular the uplink of the
infrastructure-based Cognitive Radio Networks
(CRNs), through proposing a power allocation
technique based on Particle Swarm Optimization
(PSO) for MIMO systems in CR networks.
Maximum-Minimum Throughput-based Power
Assignment (MMTPA) and Equal Power
Assignment (EPA), using the average minimal
throughput among all SUs and also the complexity
to measure performance, approaches are used for
comparison. The simulation results show that the
proposed approach outperforms the two other
schemes, in addition, an increase in the total
throughput of SU system.
KEY WORDS: Cognitive radio networks, MIMO,
Particle Swarm Optimization, and Throughput
.
I. INTRODUCTION
Due to the rapid growth of wireless technologies,
the increasing demand for higher bandwidth
applications and the crowded spectrum. CR had
been introduced by J. Mitola [1], as a promising
solution. CR has the ability to utilize efficiently
the spectrum as its network entities are aware of
their environment and can communicate
intelligently in such a manner that their
transmission and reception parameters can be
changed dynamically avoiding the interference
with licensed or unlicensed users [2-4]. Therefore,
CR has attracted much attention from researchers
[5-8].
Actually, there are three main approaches for CR
to exploit the available spectrum bands named
underlay spectrum sharing, overlay (opportunistic
spectrum access) and sensing-based spectrum
sharing, respectively. For underlay spectrum
sharing, the spectrum band is shared among all
PUs and SUs yielding co-channel interference
between SUs and PUs and so that the transmitted
power of SUs system must be controlled in such a
way to reduce interference with PUs system and
protect the quality of service (QoS) of licensed
PUs [9-10]. From the point of view of overlay,
SUs only has the right to use the empty spectrum
bands after the process of sensing [11-12].
Sensing-based spectrum sharing is a hybrid
scheme in which SU firstly sense the spectrum,
then adjusts its transmitted power according to
PU’s status [13]. Underlay spectrum sharing is the
most common used scheme so the transmitted
power of SUs must be assigned carefully in order
to maximize SUs throughput, under power and
interference constraints, and not to affect the QoS
of PUs. Through the literature, there are many
optimization techniques that have been used to
find SUs optimal transmitted power. EPA is the
simplest one among them, where equal power is
assigned to each one of SUs. The assigned
constant power doesn’t affect the QoS of each
PUs [14]. Another scheme named MMTPA in
which [14] the minimal power of all SUs is
maximized in order to have the same QoS of each
SUs.
In fact, OFDM has been recognized as the
promising candidate in Cognitive Radio Network
(CRN) due to its flexibility of resource allocation.
2016 International Conference on Selected Topics in Mobile & Wireless Networking (MoWNeT)
978-1-5090-1743-0/16/$31.00 ©2016 IEEE
In seeking to have a superior data throughput and
more reliability, Multiple-Input-Multiple-Output
(MIMO) [15-16] has been adopted in modern
communication systems without using additional
transmit power or spectral bandwidth because of
its ability to exploit space diversity. For this
reason, researchers had been implemented MIMO
system for CRNs to improve the performance of
SUs under cochannel interference [17-18].
In this paper, a proposed power allocation
technique based on PSO will be suggested to
maximize the minimal throughput among SUs
considering that the deduced interference from
each one of SUs should be under the allowed
threshold level and guaranteeing the QoS of the
user of Pus and SUs. Therefore, we use the
capacity pound of SUs under co-channel
interference to measure the SUs throughput. In
fact, there are different required QoS for different
SUs and consequently variable throughput for
each SU. The minimal throughput among SUs
will be used to quantify system performance. For
comparison, the minimal throughput gained by
PSO will be compared with that achieved by
MMTPA and EPA. Simulation results show that
the proposed PSO power allocation scheme
outperforms the EPA and MMTPA as it gives the
maximum minimum throughput and also
maximize the throughput of each one of SUs but
with more complexity.
The rest of this paper is organized as follows: the
system model and throughput calculation are
presented in Section II. Where MMTPA and EPA
schemes will be discussed in Section III. The
proposed PSO power optimization scheme is
highlighted in Section IV. Simulation results will
be presented in Section V. Finally, a conclusion
will be in Section VI.
II. SYSTEM MODEL AND
THROUGHPUT CALCULATION
A. SYSTEM MODEL
Suppose a CRN consist of ܭ cochannel SUs
coexist with ܮ PUs pair in the same spectrum band
and share the same frequency band in underlay
paradigm. Each ܷܵ
(the ݅
௧௛
SU) has a single
MIMO transmitter and a single MIMO receiver
(transmitter ݅ and receiver݅, respectively). Each
transmitter composes of ܯ transmit Antennas and
each receiver contains of ܰ receive antennas. For
a given SU, each transmit antenna has the same
shared transmitted power. An independent
Rayleigh flat-fading MIMO channel is assumed.
It can be represented by ሺܯܰሻ channel matrix
ofܪאԧ
ெൈே
. Now, consider a transmitted signal
vector ݔ
ݐאԧ
ெൈூ
 from transmitter݅, then for
eachܷܵ
, the received signal at the receive
antenna array can be expressed by:
ݕ
ൌܪ
ݔ
൅݊
൅ߟ
൅ߤ

(1)

Where ݕ
ൌሺݕ
௜௞
ሻאԧ
ேൈூ
represents the received
signal vector for the receiver݅, ܪ
ൌሺܪ
௜௞௝
ሻא
ԧ
ேൈெ
is the channel matrix between the
transmitter ݅ and receiver ݅ , ݊
ൌሺ݊
௜௞
ሻאԧ
ேൈூ
represents the additive white noise vector in the
channel, ߟ
ൌሺߟ
௜௞
ሻאԧ
ேൈூ
is the vector
corresponding to the co-channel interference from
other SUs and ߤ
ൌሺߤ
௜௞
ሻאԧ
ேൈூ
represents PUs
co-channel interference vector. Moreover, each
element of channel matrix ܪ
is assumed to be an
independent complex random Gaussian variable
and can be modeled
as݄
௜ேǡெ
̱ࣝࣨሺͲǡͲǤͷ݌݁ݎ݀݅݉݁݊ݏ݅݋݊ሻ.
Furthermore, we assume that a full channel state
information (CSI) is known to the receiver and is
not known to transmitter, and each element of ݊
is independent of each other.
Actually, there will be mutual co-channel
interference among PUs and SUs and to assure the
QoS of PUs the co-channel interference form SUs
should be below the given threshold which can be
expressed as:
σܲ
௜ୀ௄
௜ୀଵ
ܩ
௟ǡ௜
൑Ȳ
for ݈ͳǡʹǡǥǥǤǡܮ (2)
Where ܲ
represents the transmit power ofܷܵ
,
ܩ
௟ǡ௜
is the power gain between the transmitter of
݅
௧௛
SU and the receiver of ݈
௧௛
PU, and Ȳ
is the
introduced interference threshold value from all
SUs on ݈
௧௛
PU.
B. THROUGHPUT CALCULATION
In order to quantify the throughput, we first
calculate the capacity of MIMO system in CRN
under co-channel interference. Then, use the
capacity pound (in b/s/Hz) to determine the
throughput. For eachܷܵ
, the channel capacity,
according to [16-17], can be expressed as:
ܥ
ൌ
݀݁ݐቀܫ
ೕǡ೔
ܪ
ܪ
ܴ
ିଵ
ቁ ׊݆݅
(3)
where ܫ
represents an identity matrix, ܩ
௝ǡ௜
is the
power gain between ܷܵ
transmitter and
ܷܵ
receiver, ܪ
represents the Hermitian
transpose of the channel matrix, and ܴ
is the
correlation matrix that is given by:
ܴ
ൌ߃݊
݊
൅߃ߟ
ߟ
൅߃ߤ
ߤ
(5)
According to [18], if there are a large number of
interfering sources, the correlation matrix can be
denoted as:
ܴ
՜ߙ
൅ߚ
൅ߛ
ܫ (6)
Where, for eachܷܵ
, ߙ
represents power of
noise, ߚ
represents total co-channel interference
power from other SUs, and ߛ
represents total
interference power from PUs, respectively.
Substituting (6) into (5) with ߚ
σܲ
௜ஷ௝
ܩ
௜ǡ௝
the
channel capacity (in b/s/Hz) of ܷܵ
can be denoted
by:
ܥ
ൌ
݀݁ݐ൬ܫ
ೕǡ೔
σ
೔ಯೕ
೔ǡೕ
ାఈ
ାఊ
ܪ
ܪ
൰
(7)
Furthermore, according to [19 theorem 1], we
have
ܥ
൑ܰ
൬ͳ
ೕǡ೔
ெேσ
೔ಯೕ
೔ǡೕ
ାఈ
ାఊ
ԡܪ
ԡ
൰
(8)
In addition, applying the expectation over ܪ
elements in (8). Then, for eachܷܵ
the capacity
pound, as given in [14], can be denoted by:
߃ܥ
൑ܰ
൬ͳ
ೕǡ೔
σ
೔ಯೕ
೔ǡೕ
ାఈ
ାఊ
൰ (9)
And the maximum achievable data rate can be
determined by the right hand side of (9). Since the
throughput represents the actual date rate and
always changes with time, transceivers and
environment. It is appropriate to use the capacity
pound to quantify the throughput. Consequently,
for each ܷܵ
the throughput ܶ
(In b/s/Hz) can be
denoted by:
ܶ
ൌܰ
൬ͳ
ೕǡ೔
σ
೔ಯೕ
೔ǡೕ
ାఈ
ାఊ
൰ (10)
III. MMTPA AND EPA SCHEMES
A. MMTPA SCHEME
The aim of MMTPA is to maximize the minimal
throughput among all SUs while ensuring the total
co-channel SUS interference does not affect the
QoS of all PUs. Setting ܶ
ൌܶ
ሺሻ where
ሼܲ
represents the power set ofܲ
. Therefore,
MMTPA aims to find the optimal value of the
power set ܲ
כ
ൌሼܲ
כ
which satisfies the
following optimization problem:
Problem 1
Maximize ܶ
ሺܲሻ
Subject to σܲ
ܩ
௟ǡ௜
൑Ȳ
݈
௜ୀ௄
௜ୀଵ
In fact, a proposition found in [14 proposition 1]
is implemented in order to simplify the process of
finding the optimal value
כ
. According to [14],
for an uplink infrastructure-based CRN where all
SUs are connected to the same base station (BS),
therefore, the noise power and the interference
power from all PUs to all SUs’ receivers will be
constant as all SUs’ receivers are located in the
same BS i.e. ߙ
ൌߙ
ൌߙ,ߛ
ൌߛ
ൌߛ and
σܲ
ܩ
௜ǡ௞
σܲ
ܩ
௝ǡ௞
. Therefore, the
optimization problem had reduced to simply
optimizing the transmit power of the
representative SU in order to maximize its
throughput and keep the ratio of the transmit
power of any other SU to the representative SU
constant. Therefore, problem 1 can be
reformulated to be:
Problem 2
Maximize
ೝǡೝ
௄ିଵீ
ೝǡೝ
ఈାఊ
Τ
Subject to ܲ
σܨ
ܩ
௟ǡ௜
൑Ȳ
௜ୀ௄
௜ୀଵ
for all l
where ܩ
௥ǡ௥
represents the power gain between the
transmitter and the receiver of the representative
SU, ܲ
is the transmit power of representative SU
and ܨ
ൌܩ
௥ǡ௥
ܩ
௜ǡ௜
. Further, in order to
maximizeܲ
, the author [14] chooses ܷܵ
such
that
ܩ
௥ǡ௥
൑ܩ
௜ǡ௜
for all i (11)
Figure 1. Flowchart of PSO algorithm.
And make the power assignment as follows:
ܲ
כ
ൌ
൛Ȳ
σܨ
ܩ
௟ǡ௜
௜ୀ௄
௜ୀଵ
Τ (12)
ܲ
ൌܲ
כ
ܨ
for all i (13)
Substituting (12) and (13) into problem 2, the
minimal throughput, among all SUs, achieved by
MMTPA approach can be denoted by:
ܶ
ெெ்௉஺
ܰ
ቆͳ
ೝǡೝ
௄ିଵ
ೝǡೝ
ఈାఊ୫୧୬
σி
೗ǡ೔
೔స಼
೔సభ
Τ
(14)
B. EPA SCHEME
For EPA, equal level of transmitting power will
be assigned to each SU. Which means that
ܲ
ൌܲ
ܲfor all ്݆݅ (15)
Inserting (15) into (2), we further have
ܲൌ
൫Ȳ
σܩ
௟ǡ௜
௜ୀ௄
௜ୀଵ
Τ (16)
Substituting (16) into (10) yields the minimal
throughput among all SUs, which can be achieved
by EPA scheme, that can be expressed as:
ܶ
ா௉஺

ܰ
ͳ൅
ೕǡ೔
σ
ೕǡ೔
ାఊ
୫୧୬
σ
೗ǡ೔
೔స಼
೔సభ
Τ
೔ಯೕ
ቇ൱ൡ
(17)
IV. PROPOSED PSO POWER
OPTIM IZATION SCHEME
PSO algorithm is a population based global search
evolutionary algorithm inspired by the behavior
of birds and fish swarms. It was first introduced
by Dr. Kennedy and Dr. Eberhart in 1995 [20-21].
It is benefiting from the information shared
among swarm members to find the optimum
position of food, the right path to go through or
the optimum value of the optimization problem.
In PSO, the Population is called a Swarm and each
swarm member is called a Particle. Each particle
represents a possible solution of the objective
function/ optimization problem in the solution
search space. The algorithm starts with
initializing each particle with a random position
and velocity in the search space and defining
system parameters such as maximum number of
iterations, number of dimensions, swarm size and
acceleration coefficient. Then each particle
position is used as an input to fitness/objective
function to get fitness value.
A personal best (pbest) and a global best (gbest)
variables are defined that represent the particle
self-trust and social trust, respectively. Within an
iteration, the velocity and the position of each
particle are updated and the fitness value is then
evaluated using the updated position value and
compared to the current (pbest) and (g best)
values to update their values. These steps are
repeated until the algorithm finds the optimum
value of the optimization problem, the maximum
number of iterations is reached or the stopping
Figure 2. The average minimal throughput (in bps/Hz)
versus the number of SUs.
condition is achieved. Flowchart is shown in
Figure 1.
The optimization problem denoted by problem 1
aims to maximize the minimum throughput
among all SUs by optimizing the transmit power
of the SU while ensuring the QoS of PUs. Since
PSO is used as a minimization algorithm, the
problem defined in problem 1 should be
reformulated to be compatible with PSO.
Therefore, problem 1 can be reformulated to:
Problem 3
Minimize

െͳൈܰ
ቆͳ
ܲ
ܩ
௝ǡ௜
σܲ
௜ஷ௝
ܩ
௜ǡ௝
൅ߙ
൅ߛ
ቇቋ
Subject to σܲ
ܩ
௟ǡ௜
൑Ȳ
݈
௜ୀ௄
௜ୀଵ
Moreover, the complexity is an important issue
for power allocation besides to the performance.
The PSO can be implemented without knowing
any power gain among SUs. It is only compute the
minimal throughput in each iteration and adjust
the transmit power of each SU according to the
constraint defined in problem 3. On the other
hand, MMTPA needs to know all power gains
among SUs and compute the power level of each
SUs by (12) and (13). EPA needs only to compute
the common power level using equation (16)
without knowing any power gain among SUs.
Table 1.
ARTHIMETIC OPERATION NUMBER REQUIRED FOR
DIFFERENT POWER ASSIGNMENT SCHEMES.
Therefore, EPA yields the lowest complexity
among power assignment schemes. Table1 shows
the required number of arithmetic operation for
power assignment schemes used in an uplink
infrastructure-based CRN. Considering V
iterations is executed in PSO, it can be showed
that EPA requires the least number of arithmetic
operation while PSO requires more number of
arithmetic operation than MMTPA.
V. SIMULATION RESULTS
For the uplink of the infrastructure-based CRN
the power gain between transmitter l and receiver
i can be determined by the following
relationܵ
௟௜
ܦ
௟௜
, where ܵ
௟௜
models shadowing
factor and ܦ
௟௜
represents the distance between
transmitter l and receiver i and Ȝ is the path-loss
exponent.
Table 2. SIMULATION PARAMETERS.
In our simulation, all shadowing factors will be
considered to be independent lognormal random
variables with mean equals to 0-dB and 2-dB
variance. Moreover, a constant transmit power
level of 10-dB is assigned to all PUs. The rest of
simulation parameters are set as shown in Table
2.
The average minimal throughput (in bps/Hz)
against the number of SUs, the number of PUs,
PSO MMTPA EPA
Addition K-1 K-1 K-1
Multiplication VK 2K-2
Division V(K-1) K-1+L L
Simulation
parameter
Value Simulation
parameter
Value
No. of PUs (L) 2 Swarm size 100
No. of SUs (K) 4 Max. Iterations 100
No. of antennas
(N)
4 No. of
dimensions
1
Noise power
(Į)
-10-dB C1, C2 2
Threshold level
(Ȳ)
1-dB Inertia weight
(߱)
0.8
Path-loss
exponent Ȝ
4 Stopping
criteriaߝ ͳͲ
ି଺
Figure 5. The average minimal throughput (in bps/Hz)
versus noise power (dB).
Figure 6. The average minimal throughput (in bps/Hz)
versus interference threshold
(
dB
)
.
Figure 3. The average minimal throughput (in bps/Hz)
versus the number of PUs.
Figure 4. The average minimal throughput (in bps/Hz)
versus the number of number of receivin
g
antennas.
the number of receiving antennas, the noise power
(in decibel), and threshold interference level (in
decibel) is plotted in figures 2-6, respectively. In
addition, the proposed PSO scheme gives higher
performance compared to MMTPA and EPA in
the minimal throughput (among all SUs) and
outperforms them. In Figure 2, as the number of
SUs increases, the minimal throughput decreases.
This coincides with the results presented in
Equation (10). As the number of SUs increases,
the aggregated co-channel interference from other
SUs increases yielding less throughput. The
results in Figure 3 show that the increment in the
number of PUs has a relatively slight decrease in
the achieved minimal throughput.
This is because of the fixed relatively large
threshold level of 1-dB for all PUs. Therefore, the
assigned transmit power for each SUs is relatively
high which produces interference from all SUs
larger than that of PUs interference. For a MIMO
system, the more usage of receive antennas, the
more throughput will be achieved. That is what
Figure 4 proves as the number of receiving
antennas increases, the average minimal
throughput increases. The effect of a noise power
level (in dB) is shown in Figure 5 where noise
power level does not make a significant effect on
the average minimal throughput.
This is because of the small number of SUs and
PUs used in our simulation and in this case the
interference dominates the average minimal
throughput.
In addition, Figure 6 presents the impact of
interference threshold level on the average
minimal throughput where the increase in
threshold level leads to an increase in the average
minimal throughput until it reaches a saturation
level. As the threshold level increases, the
influence of PUs noise-interference will be
negligible compared to SUs noise-interference
influence that makes the average minimal
throughput reaches saturation.
VI. CONCLUSION
In this paper, a proposed power assignment
scheme based on the PSO algorithm was used to
maximize the minimal throughput among all SUs
in CRNs. In particular, in the uplink
infrastructure-based MIMO CRN where underlay
spectrum sharing paradigm is applied for
spectrum sharing between SUs and PUs. Where
the usage of a MIMO system ensures a high
system capacity and reliability. The proposed
scheme aims to optimize the transmit power of
SUs while assuring the QoS of PUs. It was
compared with two other power assignment
schemes named MMTPA and EPA using the
average minimal throughput among all SUs as
performance measurement parameter. Simulation
results showed that the proposed scheme gives
higher performance compared to MMTPA and
EPA in minimal throughput. Moreover, it
increases the total SUs throughput than other
schemes. The issue of complexity was considered
where EPA results in the least complexity and the
least average minimal throughput and PSO yields
the best average minimal throughput than
MMTPA and EPA however, with more
complexity.
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  • Book
    This book provides a broad introduction to Cognitive Radio, which attempts to mimic human cognition and reasoning applied to Software Defined Radio and reconfigurable radio over wireless networks. It provides readers with significant technical and practical insights into different aspects of Cognitive Radio, starting from a basic background, the principle behind the technology, the inter-related technologies and application to cellular and vehicular networks, the technical challenges, implementation and future trends. The discussion balances theoretical concepts and practical implementation. Wherever feasible, the different concepts explained are linked to application of the corresponding scheme in a particular wireless standard. This book has two sections: the first section begins with an introduction to cognitive radio and discusses in detail various, inter-dependent technologies such as network coding, software-based radio, dirty RF, etc. and their relation to cognitive radio. The second section deals with two key applications of cognitive radio – next generation cellular networks and vehicular networks. The focus is on the impact and the benefit of having cognitive radio-based mechanisms for radio resource allocation, multihop data transmission, co-operative communication, cross-layer solutions and FPGA-level framework design, as well as the effect of relays as cognitive gateways and real-time, seamless multimedia transmission using cognitive radio.
  • Conference Paper
    Full-text available
    Cognitive Radio (CR) technology is one of the strong candidate technologies to solve the spectrum scarcity problems. In this paper, we tackle the problem of secure data transmission between a secondary user transmitter and receiver through a relay in the presence of an eavesdropper in a cognitive radio network. The proposed scheme selects the best Decode-and- Forward relay among different relays to assist the transmitter, and to maximize the achievable secrecy rate without harming the primary user. Simulation results show that the secrecy capacity of the network using this scheme will almost be double the capacity when selecting the conventional scheme of relay selection.
  • Article
    The maximum–minimum-throughput-based power assignment (MMTPA), which results in the maximum–minimum throughput among all secondary users (SUs) while assuring the quality of service (QoS) of each primary user (PU), is proposed for multiple-input–multiple-output (MIMO) systems in cognitive radio (CR) networks. We first derive the capacity bound of the MIMO system under cochannel interference and use this capacity bound to quantify the throughput. Instead of employing the traditional optimization technique, we present a proposition to simplify the optimization of the power assignment. In particular, in the uplink of the infrastructure-based CR network, the problem can be reduced so that the MMTPA only has to optimize the transmit power of the representative SU and keep the ratio of the transmit power of each SU to that of the representative SU constant. For comparison, we also propose two other power assignments, namely, the equal power assignment (EPA) and the iterative power control (IPC). As expected, we found from simulation results that MMTPA outperforms EPA and IPC in the minimal throughput among all SUs.
  • In this study, the physical layer security for cognitive radio network (CRN) will be investigated in which a secondary user transmitter (SU-Tx) sends confidential information to a SU receiver (SU-Rx) on the same frequency band of a primary user (PU) in the presence of an eavesdropper receiver. Moreover, relay selection scheme is proposed for the security constrained CRNs with single eavesdropper, multiple eavesdroppers and PUs. The proposed scheme selects a trusted decode and forward relay to assist the SU-Tx and maximise the achievable secrecy rate that is subjected to the interference power constraints at the PUs for the different number of eavesdroppers and PUs under available channel knowledge. The SU cooperates with relays only when a high secrecy rate is achieved. Secrecy rate and secrecy outage probability are the two performance metrics that are used to verify the effectiveness of the proposed scheme although asymptotic approximations of the secrecy outage probability are also derived. Simulation and analytical results demonstrate that the performance improvement of the proposed scheme reaches to the double relative to the conventional scheme for the secrecy capacity.
  • Article
    Full-text available
    Recently, cognitive radio (CR) access has received much attention to overcome spectrum scarcity problem. Spectrum sensing methods are often used for finding free channels to be used by CR. In this paper, the problem of cooperative spectrum sensing will be investigated in CR networks over realistic channels. This problem is not clarified until now by taking into account the error effect on the decision reporting. The analytical expressions of the hard and softened one bit and two bits hard combination scheme for cooperative spectrum sensing will be derived. These expressions are investigated to compare with simulation results. The analysis and simulation results show that the performance of cooperative spectrum sensing is limited by the probability of reporting errors. Moreover, it is shown that there is a significant performance loss when a final decision regarding to primary user’s (PU) state made at the fusion depends on a set of local spectrum sensing information that are distorted by imperfect reporting channels during transmission. The probability of detection is degraded due to imperfect reporting channel by 16.5% and 12.2% with one bit hard combination and softened two bits hard combination, respectively. To reduce this performance loss, Amplify and Forward (AAF) relying mechanism will be proposed. The probability of detection is improved by 8% and 9.3% with one bit hard combination and softened two bits hard combination, respectively using AAF relaying mechanism.
  • Article
    Full-text available
    will be investigated. This method is cooperative spectrum sensing which is based on energy detection in CR networks in order to overcome fading, noise and shadowing effects on individual CR user and increase the reliability and efficiency of spectrum sensing. The sensing information from CR users combines at the Fusion center (common receiver) by soft combination or conventional hard combination techniques. Soft combination has excellent performance but, it requires a lot of overhead for feedback observation. In contrast, the conventional hard combination scheme requires only one bit of overhead, but it has worst performance because of loss of information caused by local hard decisions. In this paper, the detection performance improves by increasing the levels of local observations by proposing three-bit softened decision scheme which is not clarified until now. Analytical Expressions of proposed three bits scheme will be derived. Design parameters are optimized to fmd the detection performance with a given false alarm probability. The simulation results show that, at 90% detection probability, the cooperative signal detection improves by 1.75 dB of signal to noise ratio over conventional hard combination (Le., one bit scheme) . Moreover, comparisons between proposed scheme, one bit and two bits schemes will be made.