Optimization of Cognitive Radio spectrum and 1. To optimise maximum throughput and SNIR of secondary user’s w.r.t Primary user’s. 2. To calculate throughput w.r.t no of slots by varying time slots and channel bandwidth. 3. To study the performance characteristics achieved through Greedy Algorithm and Optimal algorithm.

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2.  INTRODUCTION
 COGNITIVE RADIO TECHNOLOGY
 DYNAMIC SPECTRUM ACCESS
 POMDP
 LITERATURE REVIEW
 PROBLEM FORMULATION
 OBJECTIVE
 PLANNING OF WORK
 FACILITIES REQUIRED
 PROPOSED PLACE OF WORK
 REFERENCES
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6. An enhancement on the
traditional software radio
concept wherein the radio is
aware of its environment and
its capabilities, is able to
independently alter its physical
layer behavior, and is capable
of following complex
adaptation strategies.

7.  A Cognitive radio can automatically
change its parameters based upon the
interaction with the environment in which
it operates.
 It is an independent unit in communication
environment .
 Frequently exchanges info with n/w it is
able to access as well as with other CR’s .
 CR is an SDR that additionaly
 senses its environment.
 Tracks senses.
 Reacts upon its findings .

8.  Main aspect: One main aspect
of cognitive radio is related to
autonomously exploiting
locally unused spectrum to
provide new paths to
spectrum access.
Power
Time
Frequency
Spectrum in use by Primary
user
Spectrum Hole

9. MAJOR
FUNCTIONS OF
COGNITIVE RADIO
SPECTRUM
SENSING
SPECTRUM
MANAGEMENT
SPECTRUM
SHARING
SPECTRUM
MOBILITY

10.  The most important task .
 It is detecting the unused spectrum .
 Sharing it without harmful interference with other users.
 It is an important requirement of the Cognitive Radio network to sense spectrum holes.
 Detecting primary users is the most efficient way to detect spectrum holes. Spectrum
sensing techniques can be classified into three categories:
 Transmitter detection: cognitive radios must have the capability to determine if a
signal from a primary transmitter is locally present in a certain spectrum. There are
several approaches proposed:
 Matched filter detection
 Energy detection
 Cyclostationary feature detection
 Cooperative detection: refers to spectrum sensing methods where information from
multiple Cognitive radio users are incorporated for primary user detection.
 Interference based detection.
 However, a sensing method should be able to identify the presence of primary user
within a certain duration to avoid interference.

11.  Spectrum sensing schemes may be broadly categorized into two different
approaches: proactive or reactive
 In proactive sensing
 The spectrum is periodically sensed & the information is maintained.
 While in the reactive approach
 The sensing operations are carried out on an on-demand basis .
 A CR user starts to sense the spectrum only when it has some data to transmit.
 The trade-off between periodic and on-demand overheads (both lead to delays) is
carefully analyzed.
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12.  How and which channel
should be accessed.
 Minimum interference by
secondary user’s.
 Must sense radio frequency
band frequently.
 When Primary users are
detected they must
immediately vacate the
channel .

13.  LOCATE UNUSED SPECTRUM.
 CHARACTERISE THE PRESCENCE OF PRIMARY
USERS.
 ORGANISE USERS TO OPERATE WITHIN THE
SPECTRUM IDENTIFIED.
 ENSURES NO INTERFERENCE TO THE OTHER
USERS BY SCANING AND SENSING

14.  PARTIALLY OBSERVABLE :
 Means CR users can select set of channels to be sensed and set
of channels to be accessed based on sensing outcome.
 System state is not directly known however CR user’s observe to
learn.
 Observation yields current state.
 Information state is updated after each action and observation.
 In POMDP policy maps the information states into actions and
maximizes the expected total reward.
 It permits the uncertainity rearding the state of a Markov process
and allows the state information acquistion.
 In POMDP learner is not allowed to observe each state directly
but only through messages containing information about the state.
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15.  Spectrum contains a number of channels. ( who
have authority to use it).
 However when channel is not used , SU’s can
access the channel with prior to observe whether
channel is available to avoid interference to PU’s.
 Group of SU’s will sense and monitor primary
channels depending upon the time step and will
switch from occupied and unoccupied according to
the Markov chain.
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17. • Secondary user senses each set of channels at the beginning of each
slot.
• Based on the sensing outcomes , SU’s will decide which channel
should be accessed.
•It then transmits the data on channel selected.
• At the end of each time slot , SU will send acknowledgement signal
that will indicate successful transmission.

18. Sr No. Topic Literature Review
1. Cognitive
Radio’s
Ian F. Akyildiz et al. [2006], Q.Zhao et.al [2007] , Senhua Huang et al. [] ,
S. Gerihofer et al [2007] , K. Lee et al[2007] , Ying-Chang Liang et.al
[2008], Ian F. Akyildiz et al.[2009], Ayman A. El-Saleh, et.al [2009]
2. Dynamic
Spectrum
Access
Lin Xu et.al[2000], Milind M. Buddhikot et.al[2005], Chunyi Peng et.al
[2006], Raul Etkin et.al[2007], Qing Zhao et.al [2007]
3. POMDP R.Smallwood et al.[1973] , Xi-Ren Cao et al[2004]

19.  R.Smallwood et.al [1973]
This paper formulated the optimal control problem for a class of mathematical models
in which the system to be controlled was characterized by a finite-state discrete-
time Markov process. The states of this internal process were not directly
observable by the controller; rather, they had an available set of observable outputs
that were only probabilistically related to the internal state of the system. The
formulation was illustrated by a simple machine-maintenance example, and other
specific application areas were also discussed. The paper demonstrated that, if
there were only a finite number of control intervals remaining, then the optimal
payoff function was piecewise-linear, convex function of the current state
probabilities of the internal Markov process. In addition, an algorithm for utilizing
this property to calculate the optimal control policy and payoff function for any
finite horizon was outlined. These results were illustrated by a numerical example
for the machine-maintenance problem.
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20.  Lin Xu et.al [2000]
They aimed at resolving the current inefficient usage of spectrum band and made the radio
that had ability to intelligently reorganize the status of the radio spectrum environment and
adaptively changed its transmission parameters such as: Transmission frequency , Bandwidth ,
Power efficiency , Modulation schemes.
 Xi-Ren Cao et.al[2004]
Their main observation was that the reward history in a POMDP contained information about
the distribution of the unknown state which lead to problem formulations for POMDPs
depending on whether the reward function were known and whether the reward at each step
was observable. The policy depending on both the observation and reward histories was named
as a reward information (RI) policy. The study in this paper demonstrated the fundamental
difference between the analytical approaches (no observation on reward was made) and the
learning based approaches in the POMDP framework. Finally, the same idea was applied to the
observation.

21.  Milind M. Buddhikot et.al [2005]
In this paper they argued that a simple pragmatic approach, that offers coordinated spatially
aggregated spectrum access via regional spectrum broker will be more attractive in immediate
future. They invented two new concepts CAB (Coordinated Access Bands) and SMA (
Statistically Multiplexed Access) to spectrum that forms the basis of their works. They then
described their implementation in the new DIMSUMnet network architecture consisting of
four elements : Base stations , clients , radio access network manager (RANMAN) which
obtained the spectrum leased and a per domain spectrum broker that controlled the spectrum
access. They also described DIMSUM Relay cluster – a multihop all wireless architecture to
illustrate the application of coordinated DSA to fixed wireless access and mesh networks.
They also highlighted the issue in the deployment model and spectrum allocation.
 Chunyi Peng et.al [2006]
In this paper, they defined a general model and utility functions for optimizing utilization and
fairness in spectrum allocation for open spectrum systems.Their experimental results proved
that not only can their algorithms drastically improve network performance by reducing
interference, but their distributed algorithm provides benefits comparable to the centralized
approach while drastically reducing computation complexity. To reduce communication
overhead, they developed a rule based spectrum management scheme where users observed
local interference patterns and acted independently according to preset spectrum rules. They
also examined the impact of variations in spectrum availability and bandwidth distributions on
their algorithms

22.  Ian F. Akyildiz, et al. [2006]
In this survey, intrinsic properties and current research challenges of the xG networks were
presented. xG networks, equipped with the intrinsic capabilities of the cognitive radio,
provides an ultimate spectrum-aware communication paradigm in wireless communications.
The unique challenges in xG networks are investigated by a bottom-up approach, starting from
the capabilities of cognitive radio techniques to the communication protocols that need to be
developed for efficient communication. Moreover, novel spectrum management functionalities
such as spectrum sensing, spectrum analysis, and spectrum decision as well as spectrum
mobility are introduced. The discussions provided in this survey strongly advocate spectrum-
aware communication protocols that consider the spectrum management functionalities.

23. Senhua Huang et al.
In this paper, study of the data capacity of cognitive radio users in opportunistic spectral access under
stringent intrusion constraints on collision probability and the overlapping collision time is carried
out. Two random access schemes were proposed for cognitive radio devices to exploit the spectral
opportunities in primary bands. Closed-form expressions for the collision probability of the PU and
the capacity of the SU are obtained and it is shown that the proposed random access schemes have
similar capacity performance. Furthermore, it is also concluded that the collision probability and the
overlapping time constraints are closed related. SUs can utilize a significant amount of spectral
vacancy in the primary band under overlapping time constraint when appropriate. In addition, we
consider the overhead cost in the SUs’ packets and demonstrate that an optimal tradeoffs can be
obtained for exponential and fixed packet length. Finally, we investigate the aggregated throughput
performance and collision probability in a multi-band multi-secondary system. Our work provides a
better understanding on the fundamental properties and performance limit of opportunistic spectrum
access. Future works in this direction may involve the extension to systems with inaccurate sensing
devices. Imperfect sensing will induce collisions between different SUs and hidden nodes problems
in networks. Advanced signal processing techniques in physical layers can also be integrated in the
design of media access schemes.

24.  K.Lee et.al[2007]
In this paper, they have considered a wireless communication scenario where multiple
unlicensed cognitive users seek to access unused frequency channels licensed to primary users.
They proposed a simple and fully distributed cooperative spectrum sharing strategy in which
cognitive users collaborate by sharing their channel occupancy information. They also
observed that the throughput of the cognitive users were improved and the interference to the
primary users were less as a result of the cooperative sensing strategy. These performance
benefits required only a small price to pay in the form of a slight increase on the probability of
missed opportunity for access and a mild overhead brought by a common control channel used
for sharing the spectrum information.

25.  Ian F. Akyildiz et al.[2009]
The discussions provided in this survey strongly advocated cooperative spectrum-aware communication
protocols that considered the spectrum management functionalities. The cross-layer design requirement
necessitated rethinking of the existing solutions developed for classical wireless networks. A particular
emphasis was given to distributed coordination between CR users through the establishment of a common
control channel. Moreover, the influence of these functions on the performance of the upper layer protocols,
such as the network layer, and transport layer protocols were investigated and open research issues in these
areas were also outlined. Finally, a new direction called the commons model has been explained, where
CRAHN users independently regulated their own operation based on pre-decided spectrum etiquette.
 Ayman A. El-Saleh, et.al [2009]
In this paper, the throughput-sensing time relationship in local and cooperative spectrum sensing was
investigated under two distinct scenarios, namely, constant primary user protection (CPUP) and constant
secondary user spectrum usability (CSUSU) scenarios. The performance was then characterized through the
normalized capacity versus sensing time relationship for both local and cooperative sensing. The simulation
results proved that in local sensing under CPUP transmission mode, the maximum SUs capacity was achieved
at a unique optimal sensing time. It was also found that increasing the protection level of PUs leads to increase
the required optimal sensing time and reduces the achievable maximum capacity. In cooperative sensing under
CPUP as well, the performance of SUs network were be improved by cooperating more users in the network.
In local sensing under CSUSU mode, it was observed that there is no optimal sensing time at which the SU
capacity can be maximized. The SU capacity continuously decreased with increasing the sensing time as well
as increasing the protection level of PUs. In cooperative sensing, it was found that cooperating more users in
the network had no effect if the sensing slot exceeds 5% of the total frame duration. In this research, some
parameters were assumed to be constants such as the SNR values of PUs and total frame duration. The
simulation results proved that the design of sensing slot duration was very critical and depending on the
number of cooperating users under CPUP scenario whereas under CSUSU, cooperating more users had no
effect if the sensing time used exceeded 5% of the total frame duration.

26.  CR networks are envisaged to solve the problem of spectrum scarcity by making
efficient and opportunistic use of frequencies reserved for the use of licensed users
of the bands.
 To realize the goals, the CR devices need to incorporate the spectrum sensing,
spectrum decision, spectrum sharing, and spectrum mobility functionalities.
 Cognitive radios offer the promise of being a disruptive technology innovation that
will enable the future wireless world. Cognitive radios are fully programmable
wireless devices that can sense their environment and dynamically adapt their
transmission waveform, channel access method, spectrum use, and networking
protocols as needed for good network and application performance.
 There is however a big gap between having a flexible cognitive radio, effectively a
building block, and the large-scale deployment of cognitive radio networks that
dynamically optimize spectrum use. Building and deploying a network of
cognitive radios is a complex task.
 There is a growing concern that conventional academic research in this area has
reached a point of diminishing returns and that further progress in the above areas
will depend on a new approach involving multi-institutional research teams
working with real-world experimental deployments of cognitive radio networks.
 Many researchers are currently engaged in developing the communication
technologies and protocols required for CR’s. However, to ensure efficient
spectrum-aware communication, more research is needed along the lines
introduced in this survey.

27.  To optimise maximum throughput and SNIR of secondary user’s
w.r.t Primary user’s.
 To calculate throughput w.r.t no of slots by varying time slots and
channel bandwidth.
 To study the performance characteristics achieved through Greedy
Algorithm and Optimal algorithm.
 To compare the results achieved through Greedy algorithms and
Optimal algorithms.

28. The present research work is proposed to be completed in following
three stages:
 Literature review.
 Simulation work using MatLab Software.
 Documentation.

29.  Lab
 Matlab 7.5
 Refered Journals
 Science Direct Papers

30. [1] Rao, T.V., Rao, G.P. and Reddy, K.H.C., 2008. Experimental
Investigation of Pongamia, Jatropha and Neem Methyl Esters as
Biodiesel on C.I. Engine. Jordan Journal of Mechanical and
Industrial Engineering, 2(2): 117-122.
[2] “Opportunistic Spectrum Access in Cognitive Radio Networks ’’
Senhua Huang, Xin Liu, and Zhi Ding ; 1-9
[3]R.Smallwood and E.Sondik “ The optimal control of partially
observable Markov Processes over Finite Horizon” Operation
Research , Vol 21 no 5 , p1071-1088, 1973.
[4] L.Xu , R.Tonjes , T.Paila , w.Hansmann, M.Frank and
M.Albrecht “ DRiVE-ing to the Internet: Dynamic Radio for IP
Services in Vehicular Environments” in Proc. 25th annual
conference on LCN , USA , 2000.

31. [5] Xi-Ren Cao and Xianping Guo “ Partially Observable Markov Decision
Processes With Reward Information” 43rd IEEE Conference on Decision
and Control , December 14-17, 2004.
[6] Milind M.Buddhikot, Paul Kolodzy , Scott Miller, Kevin Ryans , Jason
Evans “DIMSUMNet : New Directions in wireless networking using
coordinated dynamic spectrum access”, IEEE, WoWMoM05 , June 2005.
[7].I.F.Akyyilidiz , W.Y Lee , M.C.Varun , M.Shantidev “Next
Generation/Dynamic Spectrum Access/ Cognitive Radios wireless
Netwroks : A Survey” A Computer Network Journal(Elsevier) vol 50,
Issue 13 , p2127-2159 , 2006.
[8] “Utilization and fairness in spectrum assignment for opportunistic
spectrum access” Chunyi Peng · Haitao Zheng · Ben Y. Zhao , Springer ,
May 2006 , Pg no 555-576.
[9] S.Gerihofer , L.Tong, B.M.Sadler “ Cognitive Medium Access :
constraining interference based on Experimental models” IEEE journal ,
Selected Area communication , vol 26 No.1 , pg 95-105 , 2007.

32.  [10] Raul Etkin, Abhay Parekh, and David Tse “Spectrum Sharing for Unlicensed
Bands” IEEE J.Sel area Communication. Vol.25 No.3 pp517-528 , April 2007.
 [11] Q.Zha , L.tong , A.Swami , Y.Chen , “ Decentralized Cognitive MAC for
opportunistic Spectrum Access in Ad Hoc Networks : A POMDP Framework “
IEEE J , Selected area , communication Vol.25 No.3 , p589-600 , 2007.
 [12] Kyounghwan Lee and Aylin Yener “Throughput Enhancing Cooperative
Spectrum
 Sensing Strategies for Cognitive Radios” IEEE Proc of ACSSC , p2045-2049 ,
Nov 2007.
 [13] Y.C.Liang , Y.Zeng , e.Peh , a.T. Hoang “ Sensing throughput Tradeoff for
Cognitive Radios Networks” . IEEE transactions on wireless communications , Vol
7 No. 4 , p 1326-1337 , 2008.
 [14] I.F.Akyyilidiz , w.Y Lee , K.R.chowdhary “ CRAHNs: cognitive Radio ad
Hoc Networks” . Journal of Ad Hoc Networks , p 810-836 , Science Direct, 2009.
 [15] Ayman A. El-Saleh, Mahamod Ismail, Mohd. A. M. Ali, and Ahmed N. H.
Alnuaimy “Capacity Optimization for Local and Cooperative Spectrum Sensing in
Cognitive Radio Networks” World Academy of Science, Engineering and
Technology 50 2009.
 [16] Karama Hamdi, Wei Zhang, and Khaled Ben Letaief, Fellow, IEEE “Power
Control in Cognitive Radio Systems Based on Spectrum Sensing Side Information”