SSRG International Journal of Electronics and Communication Engineering (ICEHS) - Special Issue – May 2017
Throughput Maximization In Multi-Channel
Cognitive Radio Networks Using C-Has
Spectrum Access And Bayesian Method
M.Shalini
P.G Scholar
Anna university-BIT campus
ABSTRACT:
In cognitive radio networks, the spectrum
access strategy plays an important role. The
cognitive radio networks cannot achieve a maximal
throughput by using the existing techniques like
overlay, underlay strategies which are applied to
multi-channel CRN’s. In the proposed system, the
generalized access strategy is applied to the multichannel CRN’s; also completed hybrid access
strategy is derived. There are two users namely;
primary user and secondary user. The primary users
are licensed users whereas the secondary users are
unlicensed users. In the generalized access strategy,
the secondary user selects part of the channels for
sequential spectrum sensing and access and accesses
these channels based on the sensing results whereas,
it accesses all other channels directly. The two phase
optimization framework is formulated, which takes
sensing channel allocation, sensing time allocation,
and power allocation into consideration to maximize
the gross average throughput of the multi-channel
CRN. In sensing phase, the generalized access
strategy algorithm is proposed, only part of the
channels is selected for sensing to maximize
throughput. In completed hybrid access strategy
algorithm, the SU selects all the channels. To
increase the throughput of the SU the SNR parameter
is also taken into consideration. The dynamic access
strategy is applied to increase the channel sensing
and make decision on allocation of channels to SU.
Hence, the throughput of the SU is maximized by
using the proposed algorithm. The experimental
result shows that the performance is better when
compared to the existing system.
1
INTRODUCTION
The step has been taken to open the door to dynamic
spectrum access using this technology by Spectrum
regulatory Committees in many countries and for its
implementation the rules are laid down. For various
applications the standardizing and harmonization of
this technology has been striving by the International
organizations. The future of wireless communications
deemed to have fundamental influence and provides
definition of cognitive radio systems and
standardization activities on cognitive radio all over
the world, also describing the state of art in the
regulatory. The varieties of wireless communication
scenarios which can be applied to cognitive radio
concepts are described.
1.1
Definition of CRN
The different definitions are being
developed both in academia and standard bodies,
such as FCC, IEEE-1900 and the SDR Forum and
already there are several definitions of CR.
Summarizing Mitola, a full CR can be defined as
“…a radio that is aware of its surroundings and
adapts intelligently”. It requires an adaptation and
intelligence at all 7 layers of ISO model.
“Cognitive Radio System (CRs) is a radio
system employing technology that allows the system
to obtain knowledge of its operational and
geographical environment, established policies and
its internal state; to dynamically and autonomously
adjust its operational parameters and protocols
according to its obtained knowledge in order to
objectives; to learn from the results obtained”.
1.2
Radio
Functions and components of Cognitive
The next big bang in wireless
communication is expected to be the cognitive radio.
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SSRG International Journal of Electronics and Communication Engineering (ICEHS) - Special Issue – May 2017
The adaptability to wireless communication
is the main goal of cognitive radio and it is achieved
through dynamic spectrum access. The performance
is achieved in wireless communication and the
frequency spectrum utilization is enhanced. Spectrum
sensing, spectrum management, spectrum mobility
are the major functionalities of cognitive radio
system. The information about the target radio
spectrum is provided by spectrum sensing and it is
utilized by cognitive radio user. The spectrum
management is used to exploit the information
obtained from spectrum sensing to make decision on
spectrum access. The spectrum mobility will control
the changes in target spectrum and operational
frequency is changed accordingly.
1.3
Deployment scenarios
The following possible scenarios for CRS, which
are not exhaustive, nor mutually exclusive, have been
identified:




1.4
To guide reconfiguration of connections
between terminals and multiple radio
systems CRS technology is used:
To improve the management of the assigned
spectrum resources an operator of radio
communication
systems
uses
CRS
technology.
An enabler of cooperative spectrum access is
used by CRS technology.
An enabler of opportunistic spectrum is used
by CRS technology.
CRS applications
The cognitive radio is flexible and efficient and
open opportunity to enable a wide variety of emerging
applications, ranging from smart grid, public safety to
medical applications. This section presents a brief
view on how cognitive radio would support such
applications, the benefits that cognitive radio would
bring, and also some challenges that are yet to be
resolved.
2
LITERATURE SURVEY
X. Kang,et.al [1]For cognitive radio
networks, a new spectrum-sharing model called as
sensing-based spectrum sharing is proposed. The
model consists of two phases. The secondary user
(SU) listens to the spectrum allocated to the primary
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user (PU) in the first phase to detect the state of
primary user (PU). The advantage is using dual
decomposition method to achieve ergodic capacity of
SU link. It is shown that the SU can achieve a
significant capacity gain under the proposed model,
compared with that under the opportunistic spectrum
access or the conventional spectrum sharing model.
The interference power constraint is applied to
protect the PU when PU is active. Due to the
complexity of this problem, two simplified versions,
which are referred to as the perfect sensing case and
the imperfect sensing case, are used. For the perfect
sensing case, the Lagrange dual decomposition is
applied to derive the optimal power allocation policy
to achieve the ergodic capacity. For the imperfect
sensing case, an iterative algorithm is developed to
obtain the optimal sensing time and the
corresponding power allocation strategy. It is shown
that the SU can achieve a significant capacity gain
under the proposed model, compared with that under
the opportunistic spectrum access or the conventional
spectrum sharing model. The disadvantage by using
this new model is the evaluation of the ergodic
capacity of the SU is formulated as an optimization
problem over the transmit power and the sensing
time.
M. G. Khoshkholgh et.al [2] The
advantage is using a novel mixed access strategy in
which contrast to the underlay strategy, the secondary
service transmits during the idle periods without
considering the interference threshold constraint. In
contrast to the overlay strategy, mixed strategy makes
transmission during the busy periods with a
probability p_a subject to satisfying the interference
threshold constraint. Parameter p_a is a secondary
service parameter, which can be adjusted based on
the spectrum status. Moreover, the secondary service
can adjust p_a to select appropriate access strategy
with the objective of maximizing the achieved
capacity based on the interference at the secondary
service receiver, I, imposed by the primary service
transmitter.
The
proposed
spectrum-sharing
technique is developed based on I significantly
reduces the system complexity comparing to the
system in which for spectrum sharing, the imposed
interference at the primary receiver is required.
Further, suggesting a simple power allocation scheme
for the mixed strategy that its achieved capacity is
very close to the maximum achievable capacity of the
secondary service. The disadvantage is that the
access strategies are applied only to single channel
and applicable for multi-channel CRN.
H. Jiang, et.al [3] The paper investigates
the optimal sensing order problem in multi-channel
cognitive medium access control with opportunistic
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SSRG International Journal of Electronics and Communication Engineering (ICEHS) - Special Issue – May 2017
transmissions. The scenario in which the availability
probability of each channel is known is considered
first. In this case, when the potential channels are
identical (except for the availability probabilities) and
independent, it is shown that, although the intuitive
sensing order (i.e., descending order of the channel
availability probabilities) is optimal when adaptive
modulation is not used, it does not lead to optimality
in general with adaptive modulation. The advantage
is using a dynamic programming approach to the
search for an optimal sensing order with adaptive
modulation is presented. For some special cases, it is
proved that a simple optimal sensing order does exist.
More complex scenarios are then considered, e.g., in
which the availability probability of each channel is
unknown. Optimal strategies are developed to
address the challenges created by this additional
uncertainty. Finally, a scheme is developed to address
the issue of sensing errors. The disadvantage is
occurrence of high computational complexity
problem which leads to minimum throughput
Y. Y. Pei, et.al [4] Energy-efficient design
has become increasingly important to batterypowered wireless devices. The paper focuses on
the energy efficiency of a cognitive radio network, in
which a secondary user senses the channels licensed
to some primary users sequentially before it decides
to transmit. Energy is consumed in both the channel
sensing and transmission processes. The energyefficient design calls for a careful design in the
sensing-access strategies and the sensing order, with
the sensing strategy specifying when to stop sensing
and start transmission, the access strategy specifying
the power level to be used upon transmission, and the
sensing order specifying the sequence of channel
sensing. The advantage is identifying the sensingaccess strategies and the sensing order to achieve the
maximum energy efficiency. And also investigates
the design when the channel sensing order is given.
Dynamic programming can be applied to identify the
optimal strategy for the parametric problem. Then, by
exploring the relationship between the two
formulations and making use of the monotonicity
property of the parametric formulation, develop an
algorithm to find the optimal sensing-access
strategies for the original problem. The disadvantage
is the stochastic sequential decision –making
problem.
3
EXISTING SYSTEM
In the existing system, the generalized
access strategy in the multi-channel CRN is used.
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The cooperative sensing becomes useful multiple
SUs perform sensing. Based on the energy efficiency,
the BS performs sensing and shares the sensing
results with SUs. It has two phases namely: sensing
phase and transmission phase. In the sensing phase,
the SUs senses the status of the primary users and
transmission phase transmits the information based
on the sensing results. The generalized access
strategy is used in the sensing phase in which only
part of the channels are sensed and the remaining
channels are allocated directly. The completed hybrid
access strategy is used to sense all the channels. The
C-HAS uses static sensing due to this delay occurs in
the allocation of channels to the secondary user’s.
The performance mainly depends on the sensing
order of the channels, sensing channel selection,
selected channels sensing time. In the sensing time
based strategy, the sensing time is optimized so that
the delay is reduced. The power allocation strategy is
applied to multi-channel CRN in which channel
estimation quality and transmission time is
considered. The sensing time and power allocation
are optimized to minimize the total energy
consumption. When the channels are sensed as idle,
the secondary user transmits the data and throughput
occurs as the optimization problem.
3.1
DISADVANTAGES:



4
The static sensing method is used.
The delay occurs due to SNR parameter.
The throughput obtained in the method is
minimum.
PROPOSED SYSTEM
An intelligent and promising solution has
been emerged for designing dynamic access spectrum
to alleviate spectrum congestion problem is obtained
by using cognitive radio (CR) networks. The key
technologies of cognitive radio networks are machine
to machine communications, vehicular networks, and
cellular networks. In a cognitive radio network
(CRN),the primary users(PUs) are referred as the
wireless devices that has licenses to access the
spectrum and the secondary users(SUs) are referred
as the wireless devices that are unlicensed. the
spectrum access strategies plays an critical roles
including the spectrum sensing methods. In
conventional methods like overlay and underlay
access strategies only the spatial or temporal
spectrum resources are utilized. The hybrid access
strategy uses secondary users (SUs), which first
detects state of channels and based on the sensing
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SSRG International Journal of Electronics and Communication Engineering (ICEHS) - Special Issue – May 2017
results it adapts its transmission power. The
significant throughput gain can be obtained by the
secondary users (SUs) using the both spatial and
temporal resources. The SUs access single channel
which is applied to single channel CRN by using
prior normal access strategies. Based on the
transmission requirements, a SU can access multiple
channels using the advanced physical technologies
like Orthogonal Frequency Division Multiplexing
(OFDM)[12]. To maximize the throughput in multichannel CRN by efficient and simple spectrum access
strategy and keeping the
Interferences on PUs under threshold is still an
problem that arises during designing.
The system has two types of phase they are
sensing phase and transmission phase. Generally in
sensing phase, the CRN senses the channels whether
it is idle or not and in the transmission phase the
transmission of information takes place from SU to
PU. While in the existing underlay access strategies
the system does not have sensing phase but to protect
the PUs transmission, the SUs directly transmit data
with low power [9]. In the overlay access strategy,
the sensing phase detects the state of channels from
SU. Only if the channel is idle it will transmit data
with high power. In the hybrid access strategy, the
states of the channels are sensed [7]. The SU will
transmit data if the channels are sensed occupied or
else transmission with full power from SU occurred.
The sensing phase senses all channels to help the SUs
to utilize channels efficiently are called as spectrum
utilization.
4.1
Generalized Access Strategy
A generalized access strategy is presented in
a multi-channel CRN smart home environment. The
part of the channel is selected by the SU for the
spectrum sensing in the sensing phase. It uses the
sequential sensing scheme. The transmission power
and channel gain is fixed based on the assumptions of
descending order of channels idle probabilities,
which is the dependency of sensing order. Then the
SU system accesses these channels based on the
sensing results in the transmission phase [11]. The
SU will access the spectrum directly, while the
remaining channels whose idle properties are below a
threshold value. To maximize the throughput the
power allocation is optimized to secondary user.
There are two subsets in the channel partition. N1 (N1
_N) is the first channel accessed via hybrid access
strategy, (N � N1) are the remaining accessed via
underlay access strategy.
The generalized access strategy is the
generalized form of existing system. It does not
combine the underlay and hybrid access strategies.



If N1=0, generalized access strategy
becomes an underlay access strategy.
If N1=N, generalized access strategy
becomes an overlay access strategy, when
the channel sensed as occupied the
transmission power is set to zero.
If N1=N, generalized access strategy
becomes an completed hybrid access
strategy, when the channel sensed as
occupied the transmission power is not set to
zero.
4.2
Completed Hybrid Access Strategy
Algorithm
In C-HAS, since the BS senses all channels,
the performance will not be affected by the selection
of sensing channels and the sensing order. It is
noteworthy that the C-HAS is not trivially simplified
from the general case when N1 = N. The analysis of
C-HAS can provide the insightful understanding of
the proposed access strategy.
4.4
Bayesian detector with high and low SNR
regions:
Bayesian detector for spectrum sensing is
used to achieve higher spectrum utilization in
cognitive radio networks. It has two hypotheses.
They are approximation at lower SNR regions and
approximation at higher SNR regions. Hence,
through this method the throughput is increased
further.
It is implemented using network simulator 2
5
Graphical Results
Fig 5.1 Throughput analysis
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SSRG International Journal of Electronics and Communication Engineering (ICEHS) - Special Issue – May 2017
REFERENCES
5
Conclusion
The generalized access strategy in a multichannel CRN smart home environment and
formulating a two-phase optimization framework to
maximize the gross average throughput has been
proposed. In the sensing to find the optimized
number of sensing channels and sensing times, first
proposed a GAS, in which not all of the channels
need to be sensed. Under the conditions some
channels have sufficient low idle probabilities or the
total number of channels is large. Then proposed a
CHAS in which all the channels are sensed. The STA
is used with low computational complexity. In the
transmission phase, the transmission powers are
optimized. In the newly proposed algorithm, the
C_HAS is used in which static sensing method is
modified as dynamic sensing method and SNR
parameter is considered. The computational
complexities are analyzed and simulations are used
for convergence analyzing. Hence in the proposed
algorithm, the simulated results indicate that the
performance is increased considerably while
comparing to the existing mechanism.
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[1] X. Kang,Y.-C. Liang ,H. K. Garg, and R. Zhang,
“Sensing-based spectrum sharing in cognitive radio
networks,” IEEE Transactions on Vehicular
Technology, vol. 58, no. 8, pp. 4649–4654, October
2009.
[2] M. G. Khoshkholgh, K. Navaie, and H.
Yanikomeroglu, “Access strategies for spectrum
sharing in fading environment: Overlay, underlay and
mixed,” IEEE transactions on mobile computing, vol.
9 no.12.pp.1780-1793, September 2010
[3] H. Jiang, L. Lai, R. Fan, and H. V. Poor,
“Optimal selection of channel sensing order in
cognitive radio,” IEEE Transactions on wireless
communications, vol. 8, no.1, pp.297-307, January
2009.
[4] Y. Y. Pei, Y.-C. Liang, K. C. Teh, and K. H. Li,
“Energy-efficient design of sequential channel
sensing in cognitive radio networks: Optimal sensing
strategy, power allocation and sensing order,” IEEE
Journal on Selected Area in Communications, vol.
29, no. 8, pp. 1648–1659, August 2011.
[5] Chao Yang, Wei Lou, Yuli Fu, Shengli Xie and
Rong Yu, “On Throughput Maximization in MultiChannel Cognitive Radio Networks via Generalized
Access
Strategy”,
IEEE
Transactions
on
Communications, 2016
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