Client Side Secure De-Duplication Scheme in Cloud Storage Environment

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 04 Issue: 09 | Sep -2017 www.irjet.net p-ISSN: 2395-0072
© 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 917
Enhancement of Throughput & Spectrum Sensing of Cognitive Radio
Networks
Snehal Lavate1, P.P.Belagali2
1Student M.E. (E&Tc), JJMCOE, Jaysinpur, Maharashtra, India
2Professor Dept. of E&Tc Engineering, JJMCOE, Jaysinpur, India
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Abstract - In cognitive radio networks, the secondary
network (users) are allowed to utilize the frequency bands of
primary network (users) when they are not currently being
used. To support this function, the secondary users are
required to sense the radio frequency environment, and once
the primary user is found to be active, the secondary users
have to vacate the channel within certain amount of time.
There are two parameters related to channel sensing:
probability of detection and probability of false alarm. The
higher the detection probability, the better the primary users
can be protected. However, from the secondary users’
perspective, the lower the false alarm probability, the more
chances the channel can be reused, thus the higher the
achievable throughput for the secondary users. In this paper,
We propose a novel cognitive radio system that exhibits
improved throughput & spectrum sensing capabilities
compared to the conventional opportunistic spectrum acces
.We study throughput of proposed system under a single high
target detection probability .Finally we provide simulation
result, in order to compare throughput of proposed &
conventional method.
Key Words: Cognitive radio, opportunistic spectrum
access, spectrum sensing, throughput maximization.
1.INTRODUCTION
The core technology that aims to alleviate the spectrum
scarcity problem in wireless communications is cognitive
radio. Cognitive radioallowaccessofunlicensed(secondary)
users to frequency bands that are allocated to licensed
(primary) users, in a way that does not affect the quality of
service (QoS) of the licensed networks [1], [2]. With the
proliferation of wireless communications technology in the
last couple of decades, in many countries, most of the
available radio spectrum has been allocated. This results in
the spectrum scarcity which poses a serious problem forthe
future development of the wireless communications
industry. On the other hand, careful studies of the usage
pattern reveal that most of the allocated spectrum
experiences low utilization. Recent measurements by
Federal Communications Commission (FCC) show that 70%
of the allocated spectrum in US is not utilized. Furthermore,
time scale of the spectrum occupancy varies from
milliseconds to hours [1]. This motivates the concept of
frequency reuse that allows secondary networks to borrow
unused radio spectrum from primary licensed networks
(users). The research in cognitiveradiohasbeen encouraged
by the measurements of the Federal Communications
Commission (FCC), which have revealed that there is a
significant amount of licensed spectrum which is largely
underutilized in vast temporal and geographic dimensions
[3]. The FCC recognizing that there is a significant amount of
available spectrum that iscurrentlynotbeingusedunderthe
current fixed spectrum allocation policy, has recently
allowed the access of unlicensed (secondary) users to the
broadcast television spectrum at locations where that
spectrum is not being used by licensed services [4]. This
unused broadcast television spectrum is often termed as
“white spaces” and has been the focus of the IEEE 802.22
WRAN standard that aims to provide broadband wireless
internet access to rural areas [5].
2. OPPORTUNISTIC SPECTRUM ACCESS
Two main approaches have been proposed for cognitive
radio so far, regarding the way that the cognitive radiousers
can access the licensed spectrum: (i) through opportunistic
spectrum access (OSA) [6], [7], according to which the
secondary users are allowed to accessa frequencybandonly
when it is detected to be idle, and (ii) through spectrum
sharing (SS) [8], [9], according to which the secondary users
coexist with the primary users under the condition of
protecting the latterfromharmful interference.Inthispaper,
we are going to focus on the former approach
Fig -1 frame structure of the opportunistic spectrum
access cognitive radio systems
The frame structure of the opportunistic spectrum access
cognitive radio systems studied so far consists of a sensing
time slot and a data transmission time slot, as depicted in
Fig.1. According to this frame structure, a secondary user
ceases transmission at the beginning of each frame and
senses for the status of the frequency band(active/idle)for 𝜏
units of time, whereas it uses the remaining frame duration
𝑇 − 𝜏 for data transmission. Therefore, an inherent tradeoff

exists in this frame structure between the duration of
spectrum sensing and data transmission, hence the
throughput of the cognitive radio system. According to the
classical detection theory [10], [11], an increase in the
sensing time results in a higher detection probability and
lower false alarm probability, which in return leads to
improved utilization of the available unused spectrum.
However, the increase of the sensing time results in a
decrease of the data transmission time, hencetheachievable
throughput of the cognitive radio system. This sensing-
throughput tradeoff was addressed in [12], where the
authors studied the problem of finding the optimal sensing
time that maximizestheaverageachievablethroughputof an
OSA cognitive radio system under a single high target
detection probability constraint fortheprotectionofthe QoS
of the primary users. In this paper, we propose a novel
cognitive radio system that overcomes the sensing-
throughput tradeoff in opportunistic spectrum access
cognitive radio networks by performing spectrum sensing
and data transmission at the same time
3. PROPOSED COGNITIVE RADIO SYSTEM
We consider the cognitive radio system that is presented in
Fig. 2. Let 𝑔 and 𝑕 denote the instantaneous channel power
gains from the secondary transmitter (SU-Tx) to the
secondary receiver (SU-Rx) and the primary receiver (PU-
Rx), respectively. The channel power gains 𝑔 and 𝑕 are
assumed to be ergodic, stationary and known at the
secondary users1 similar to [8], [9], [13], [14], [15], [17],
whereas the noise is assumed to be circularly symmetric
complex Gaussian (CSCG) with zero mean and variance 𝜎2 𝑛,
namely (0, 𝜎2 𝑛). It should be noted here that knowledge of
the precise channel power gain 𝑕 is very difficult to be
obtained in practice and therefore ourresultsserveasupper
bounds on the achievable throughput of the cognitive radio
system.
The proposed cognitive radio system operates as follows.
In the beginning, an initial spectrum sensingisperformed,in
order to determine the status (active/idle) of the frequency
band. When the frequency band is detected to be idle, the
secondary transmitter accesses it for the durationofa frame
by transmitting information to the secondary receiver. The
latter decodes the signal from the secondary transmitter,
strips it away from the received signal, and uses the
remaining signal for spectrumsensing,inordertodetermine
the action of the cognitive radio system in the next frame. At
the end of the frame, if the presence of primary users is
detected, namely if the primary users started transmission
after the initial spectrum sensing was performed, data
transmission will be ceased, in order to protect the primary
users from harmful interference. In the opposite case, the
secondary users will access the frequency band again in the
next frame. Finally, the process is repeated.
Fig -2 System model
3.1 Receiver structure
Fig -3 Receiver structure of proposed system
The receiver structure of the proposed cognitive radio
system is presented in Fig. 3. The received signal at the
secondary receiver is given by
𝑦 = 𝜃𝑥𝑝 + 𝑥𝑠 + 𝑛, (1)
Where 𝜃 denotes the actual status of the frequencyband( 𝜃=
1 if the frequency band is active and 𝜃 = 0 if it is idle), 𝑥𝑝
and 𝑥𝑠 represent the received (faded) signal from the
primary users and the secondary transmitter, respectively,
and finally 𝑛 denotes the additive noise. The received signal
𝑦 is initially passed through the decoder, as depicted in Fig.
3, where the signal from the secondary transmitter is
obtained. In the following, the signal from the secondary
transmitter is cancelled out from the aggregate received
signal 𝑦, and the remaining signal
˜ 𝑦 = 𝜃𝑥𝑝 + 𝑛 (2)
is used to perform spectrum sensing.2 This is the same
signal that the secondary receiver would receive if the
secondary transmitter had ceased data transmission, which
is the conventional way that was proposed to perform
spectrum sensing. Here, instead of using a limited amountof
time 𝜏, the whole duration of the frame 𝑇 can be used for

spectrum sensing. This way, we are able to perform
spectrum sensing and data transmission at the same time,
thus maximizing the duration of both.
3.2 Frame structure
The frame structure of the proposed cognitive radio system
is presented in Fig. 4 and consists of a single slot during
which both spectrum sensing and data transmission are
performed at the same time, using the receiver structure
presented in the previous subsection. The advantage of the
proposed frame structure is that the spectrum sensing and
data transmission time are simultaneously maximized,
whereas, more specifically, they are equal to the frame
duration 𝑇 . The significance of this result is twofold. Firstly,
the increased sensing time:
i) Enables the detection of very weak signals from the
primary users, the detection of which under the frame
structure of Fig. 1 would significantly reduce the data
transmission time, hence the throughput of the cognitive
radio network,
ii) Leads to an improved detection probability, thus better
protection of the primary users from harmful interference,
iii) Results to a decreased false alarm probability, which
enables a better use of the available unused spectrum,
iv) Facilitates the use of more complex spectrum sensing
techniques that exhibit increased sensing capabilities, but
require higher sensing time (such as Cyclostationary
detection [18] or several covariance-basedspectrumsensing
techniques [19], [20]), which prohibits their application for
quick periodical spectrumsensingundertheframestructure
presented in Fig. 1,
v) The calculation of the optimal sensing time isno longer an
issue, since it is maximized and equal totheframeduration 𝑇
vi) Continuous spectrum sensing can be achievedunder the
proposed cognitive radio system, which ensures better
protection of the quality of service (QoS) of the primary
networks.
The second important aspect is that the sensing time slot
𝜏 of the frame structure of Fig. 1 is now used for data
transmission, which leads to an increase in the throughput
of the cognitive radio network on the one hand, and
facilitates the continuity of data transmission on the other
Fig -3 Frame structure of proposed system
4. AVERAGE ACHIEVABLE THROUGHPUT OF THE
PROPOSED COGNITIVE RADIO SYSTEM UNDER A
HIGH TARGET DETECTION PROBABILITY
CONSTRAINT
In this section, we study the average achievable throughput
of the proposed cognitive radio system and compare it with
the respective achievable throughput of the cognitive radio
system that operates based on the conventional frame
structure depicted in Fig. 1. We consider, similar tothework
in [12], a single high target detection probability constraint
for the protection of the primary users from harmful
interference. Considering the fact that the priority of a
cognitive radio system is and should be the protection ofthe
quality of service (QoS) of the primarynetwork,a hightarget
detection probability is required, in order to ensure that no
harmful interference is caused to the licensed users by the
secondary network. For instance, the target probability of
detection in the IEEE 802.22 WRAN standard[5]ischosento
be 90% for a signal-to-noise ratio (SNR) as low as −20 dBfor
the primary user’s signal at the secondary detector. We
denote this target detection probability in the following by
P¯ 𝑑. More specifically, we consider as in [12] the energy
detection scheme [21] as a spectrum sensing technique, in
order to determine the status (active/idle) of the frequency
band. The detection and false alarm probability under the
energy detection scheme are given by
Respectively [12], where 𝜖 denotesthedecisionthresholdof
the energy detector, 𝛾 the received signal-to-noise ratio
(SNR) from the primary user at the secondary detector, 𝜏
denotes the sensing time and finally 𝑓𝑠 represents the
sampling frequency. For a given target detection probability
P 𝑑 = P˜ 𝑑, the decision threshold 𝜖 is given by

We can now focus on the average achievable throughput
of the cognitive radio system. The instantaneous
transmission rate of the cognitive radio system when the
frequency band is actually idle ( 𝐻0) is given by
However, considering the fact that perfect spectrum sensing
may not be achievable in practice due to the nature of
wireless communications that includes phenomena such as
shadowing and fading, we consider the more realistic
scenario of imperfect spectrum sensing, where the actual
status of the primary users might be falsely detected.
Therefore, in this paper, we also consider the case that the
frequency band is falsely detected to beidle, wheninfactitis
active ( 𝐻1). Following the approach in [15], [22], the
instantaneous transmission rate in this case is given by
Where 𝜎2 𝑝 denotes the received power from the primary
users
The average achievable throughput of the cognitive radio
system that operates based on the conventional frame
structure of Fig. 1 is given by
where ¯ 𝑅 0( 𝜏) and ¯ 𝑅 1( 𝜏) are given by
Respectively. In the equationsabove, 𝑇representstheframe
duration, P( 𝐻0) the probability that the frequency band is
idle, and P( 𝐻1) the probability that the frequency band is
active.
Under the proposed cognitive radio system, spectrum
sensing is performedsimultaneouslywithdata transmission,
whereas the sensing time and data transmission time are
equal to the frame duration 𝑇 , as seen in Fig. 4. Therefore,
the average achievable throughput oftheproposedcognitive
radio system is given by
Where ¯ 𝐶0 and ¯ 𝐶1 denote the average achievable
throughput when the frequency band is actually idle and
active (but falsely detected to be idle), respectively, and are
given by respectively.
For a target probability of detection P¯ 𝑑, we can now show
that the proposed cognitive radio system exhibits higher
average achievable throughput compared to the cognitive
radio system that operates based on the conventional frame
structure shown in Fig. 1. Following theFCCrequirementsin
[4], the secondary users should detect a worst-case SNR
from the primary users, regardless if thespectrumsensingis
performed at the receiveror thetransmitter. Thisworst-case
SNR is denoted here by ¯ 𝛾. From the classical detection
theory [10], [11], it is known that for a target probability of
detection P¯ 𝑑, the higher the sensing time, the lower the
probability of false alarm P 𝑓𝑎. Therefore, for a target
probability of detection P 𝑑 = P¯ 𝑑 and sensing time 0 < 𝜏 ≤ 𝑇 ,
it results from the equation (4) that
Considering the fact that the complementary cumulative
distribution function of the standard Gaussian 𝒬( 𝑥) is a
decreasing function of 𝑥. As a result, for a sensingtime0< 𝜏≤
𝑇, it results from the equations (8)-(14) that
i.e. that the average achievable throughput of the proposed
cognitive radio system for a targetdetectionprobability P 𝑑=
P¯ 𝑑 is higher compared to the respective of the cognitive

radio system that employs the frame structure depicted in
Fig. 1, namely it results that
For a sensing time 0 < 𝜏 ≤ 𝑇
5. SIMULATION RESULTS
In this section, we present the simulation results for the
proposed opportunistic spectrum access cognitive radio
system using the energy detection scheme as a spectrum
sensing technique. The frame duration is set to 𝑇 = 100 ms,
the probability that the frequency band is idle is considered
to be P( 𝐻0) = 0.6, whereas the sampling frequency 𝑓𝑠 is
assumed to be 6 MHz. The channels 𝑔 and 𝑕 are assumed to
follow the Rayleigh fading model and more specifically, they
are the squared norms of independent CSCG random
variables that are distributed as 𝒞𝒩(0, 1) and 𝒞𝒩(0, 10),
respectively.
In chart 1, the average achievable throughput versus the
sensing time 𝜏 is presented for the proposed cognitive radio
system (solid line) and the cognitive radio system that
employs the conventional frame structure of Fig. 1 (dashed
line), for the case of a single high target detectionprobability
constraint The received signal-to-noiseratio(SNR)fromthe
secondary transmitter at the secondary receiver is
considered to be 𝑆𝑁𝑅𝑠 = 20 dB as in [12], the target
probability of detection is set to P¯ 𝑑 = 99.99%, in order to
effectively protect the primary users from harmful
interference, whereas differentvaluesofthetargetdetection
signal-to-noise ratio from the primary user (denoted by
SNRp) are presented. One can clearly see that the average
achievable throughput of the proposed cognitive radio
system (solid line) is significantly higher compared to the
respective achievable throughput of the cognitive radio
system that employs the conventional framestructureof Fig.
1 (dashed line). This throughput improvement can be
explained by the fact that the whole duration of the frame 𝑇
is used for data transmission, asopposedtotheconventional
frame structure of Fig. 1, where only a part of the frame is
used for data transmission (i.e. 𝑇 − 𝜏). Moreover, the
improved sensing capabilities of the proposed cognitive
radio system alsocontributetothethroughputimprovement
of the cognitive radio system by enabling a more efficient
usage of the available unused spectrum. More specifically, it
can be seen from Fig. 5 and the equation (4) that for the
same target probability of detection P¯ 𝑑, the probability of
false alarm P 𝑓𝑎 for the optimal sensing time under the
conventional frame structure is higher compared to the
respective false alarm probability of the proposed cognitive
radio system. The latter remark can be explained by the fact
that the whole duration of the frame 𝑇 is used for spectrum
sensing in the proposed system, as opposed to merely a part
of the frame under the conventional framestructureofFig.1.
In chart 2, the average achievable throughput is presented
versus the target probability of detection P¯ 𝑑, for a target
detection signal-to-noise ratio from the primary user equal
to SNRp = −22 dB. It can be clearly seen from chart 2 that the
average achievablethroughputunderthe proposedcognitive
radio system is significantly higher compared to the
respective achievablethroughputofthesystemthatemploys
the frame structure presented in Fig. 1, whereas the
decrease in the average achievable throughput as the target
probability of detection P¯ 𝑑 receives higher values is small,
especially compared to the respectiveofthesecondaryusers
that employ the conventional frame structure of Fig. 1. This
means that the proposed cognitive radio systemcanprovide
better protection for the primary users on the one hand,
while achieving an increased throughput for its users on the
other, even for very high values of target detection
probability and very weak signals from the primary users.
This can be further seen from chart 3, where the average
achievable throughput is presented versus the target
detection signal-to-noise ratio from the primary users
(SNRp), for a target probability of detection equal to P¯ 𝑑 =
99.99%
Chart -1Average achievable throughputofthe proposedand
conventional opportunistic spectrum access cognitive radio
system versus the sensing time 𝜏, for various values of the
target detection SNR from the primary user(SNRp)andfora
target detection probability P¯ 𝑑 = 99.99%.

Chart -2 Average achievable throughput of the proposed
and conventional opportunistic spectrum access cognitive
radio system versus the target probability of detection P¯ 𝑑
for various values of the target detection SNR from the
primary user (SNRp)
Chart -3: Average achievable throughput of the proposed
and conventional opportunistic spectrum access cognitive
radio system versus the target detection SNR from the
primary user (SNRp) for a target detection probability P¯ 𝑑 =
99.99%.
6. CONCLUSIONS
We proposed a novel cognitive radio system that
significantly improves the achievable throughput of
opportunistic spectrum access cognitive radio systems by
performing data transmission and spectrum sensing at the
same time. More specifically, we studied the average
achievable throughput of the proposed cognitive radio
system under a single high target detection probability
constraint and showed that it can achieve significantly
improved throughput compared to the respective
conventional cognitive radio systems. Furthermore, we
provided simulation results, from which it is clear that
throughput & spectrum sensing is improved in proposed
cognitive radio system than that of conventional cognitive
radio system.
REFERENCES
[1] J. Mitola III and G. Q. Maguire, Jr., “Cognitive radios:
making software radio more personal,”IEEEPers.Commun.,
vol. 6, no. 4, pp. 13–18, Aug. 1999.
[2] S. Haykin, “Cognitive radio: brain-empowered wireless
communications,” IEEE J. Sel. Areas Commun., vol. 23, no. 2,
pp. 201–220, Feb. 2005.
[3] Federal Communications Commission, “Spectrum policy
task force report, FCC 02-155,” Nov. 2002.
[4] Federal Communications Commission, “Second Report
and Order, FCC 08-260,” Nov. 2008.
[5] IEEE 802.22 Wireless RAN, “Functional requirementsfor
the 802.22 WRAN standard, IEEE 802.22-05/0007r46,”Sep.
2005.
[6] Q. Zhao and A. Swami, “A decision-theoretic framework
for opportunistic spectrum access,” IEEE Wireless Commun.
Mag., vol. 14, no. 4, pp. 14–20, Aug. 2007.
[7] I. F. Akyildiz, W.-Y. Lee, M. C. Vuran, and S. Mohanty,
“Next generation/dynamic spectrum access/cognitiveradio
wireless networks: a survey,” Comput. Netw., vol. 50, no. 13,
pp. 2127–2159, Sep. 2006.
[8] A. Ghasemi and E. S. Sousa, “Fundamental limits of
spectrum-sharing in fading environments,” IEEE Trans.
Wireless Commun., vol. 6, no. 2, pp. 649–658, Feb. 2007.
[9] L. Musavian and S. Aissa, “Ergodic and outage capacities
of spectrum sharing systems in fading channels,” in Proc.
2007 IEEE Global Commun. Conf., pp. 3327–3331.
[10] S. M. Kay, Fundamentals of Statistical Signal Processing:
Detection Theory, vol. 2. Prentice Hall, 1998.
[11] H. V. Poor, An Introduction to Signal Detection and
Estimation, 2nd edition. Springer, 1998
[12] Y.-C. Liang, Y. Zeng, E. C. Y. Peh, and A. T. Hoang,
“Sensing throughput tradeoff for cognitive radio networks,”
IEEE Trans. Wireless Commun., vol. 7, no. 4, pp. 1326–1337,
Apr. 2008.

[13] K. Hamdi and K. B. Letaief, “Power, sensing time, and
throughput tradeoffs in cognitive radio systems: a cross-
layer approach,” in Proc. 2009 IEEE Wireless Commun.
Netw. Conf..
[14] R. Zhang, “On peak versus average interference power
constraints for protecting primary users in cognitive radio
networks,” IEEE Trans. Wireless Commun., vol. 8, no. 4, pp.
2112–2120, Apr. 2009.
[15] X. Kang, Y.-C. Liang, A. Nallanathan, H. K. Garg, and R.
Zhang, “Optimal power allocation for fading channels in
cognitive radio networks: ergodic capacity and outage
capacity,” IEEE Trans. Wireless Commun., vol. 8, no. 2, pp.
940–950, Feb. 2009.
[16] S. Stotas and A. Nallanathan, “Optimal sensing timeand
power allocation in multiband cognitive radio networks,”
IEEE Trans. Commun., vol. 59, no. 1, pp. 226–235, Jan. 2011.
[17] R. Zhang, “Optimal power control over fading cognitive
radio channels by exploiting primaryuserCSI,”inProc.2008
IEEE Global Commun
[18] J. Lundén, V. Koivunen, A. Huttunen, and H. V. Poor,
“Spectrum sensing in cognitive radios based on multiple
cyclic frequencies,” in Proc. 2007 International Conf.
Cognitive Radio Oriented Wireless Netw. Commun., pp. 37–
43. [19] T. J. Lim, R. Zhang, Y. C. Liang, and Y. Zeng, “GLRT-
based spectrum sensing for cognitive radio,” in Proc. 2008
IEEE Global Commun. Conf.
[20] Y. Zeng and Y.-C. Liang, “Spectrum-sensing algorithms
for cognitive radio based on statistical covariances,” IEEE
Trans. Veh. Technol., vol. 58, no. 4, pp. 1804–1815, May
2009.
[21] F. F. Digham, M. S. Alouini, and M. K. Simon, “On the
energy detection of unknown signals over fading channels,”
IEEE Trans. Commun., vol. 55, no. 1, pp. 21–24, Jan. 2007.
[22] Y. Chen, V. K. N. Lau, S. Zhang, and P. Ciu, “Protocol
design and stability/delay analysis of half-duplex buffered
cognitive relay systems,” IEEETrans.WirelessCommun.,vol.
9, no. 3, pp. 898–902, Mar. 2010.
[23] G. Caire, G. Taricco, and E. Biglieri, “Optimum power
control over fading channels,” IEEE Trans. Inf. Theory, vol.
45, no. 5, pp. 1468– 1489, July 1999.
[24] S. Boyd and L. Vandenberghe, Convex Optimization.
Cambridge University Press, 2004.
[25] A. Ben-Tal and A. Nemirovski, Lectures on Modern
Convex Optimization: Analysis, Algorithms,andEngineering
Applications. Society for Industrial and Applied
Mathematics, 2001.
[26] S. Boyd, L. Xiao, and A. Mutapcic, “Subgradient
methods,” 2003.
Available:
http://www.stanford.edu/class/ee392o/subgrad_method.p
df.

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