Cognitive radio network_MS_defense_presentation

Student:
Iffat Anjum
Roll: RK-554
MS 3rd Semester
Thesis Supervisor:
Md. Abdur Razzaque
Professor
May 2015
Department of Computer Science and Engineering
University of Dhaka

2
Introduction
Challenges
Design Goals
Related Work
Contribution
System Model
Proposed WF-MAC
Performance Analysis
References
Conclusion

4
[D. Goldman, “FCC scrambles to cope with data avalanche,” http://money.cnn.com/2011/12/29/technology/whitespace
spectrum/index.htm , accessed on May 2015]
[Facilitating Opportunities for Flexible, Efficient, and Reliable Spectrum Use Employing Cognitive Radio Technologies,
Federal Communications Commission (FCC), Washington, D.C. 20554, December 2003]

• Cognitive Radio (CR)
▫ Attempts to opportunistically transmit in licensed frequencies,
without affecting the pre-licensed users of these bands
▫ Its aware of its surroundings and adapts intelligently
5
Figure:CognitiveRadioNetwork
Figure: Opportunistic Spectrum Usage

6
Figure : Performance Improvements Achieved by CR

• Full Cognitive Radios do not exist at the moment and are not
likely to emerge until 2030
• Some technologies are available with some elements of CR
▫ Adaptive allocation of frequency channels in Digital Enhanced
Cordless Telecommunications (DECT) wireless telephones,
▫ Adaptive power control in cellular networks,
▫ Multiple input multiple output (MIMO) techniques,
▫ TV white space, etc.
7
[S. Pollin, M. Timmers, and L. Van der Perre, “Emerging standards for smart radios: Enabling tomorrow’s operation,” in
Software Defined Radios, 1st ed., ser. Signals and Communication Technology. Springer Netherlands, 2011, pp. 11–35.]
[S. D. R. F. Inc., “SDR: Software defined radio,” https://forums.hak5.org/index. php?/forum/81-sdr-software-defined-
radio , accessed on March 2015]

8
Cognitive radio networks are expected to be
ubiquitous and multiple CRNs often coexist
with each other
Figure: Coexistence of Cognitive Radio Networks

• When multiple CRNs operate using the same set of channels,
there is a possibility that the SUs will try to act greedily and
occupy all the available channel bandwidth.
• Miss-conception on channel occupancy.
▫ Starvation
▫ Low throughput
• The number of CR cells from multiple CRNs typically exceeds
the number of channels.
▫ Interference
▫ Repeated channel switching
• Diverse user applications produces data with
▫ Different traffic sensitivity requirements
9
Any Medium Access Control protocol should aim
at solving the problem of Coexisted CRNs enabling
them to live together maintaining QoS senstivity
and ensuring maximum resource utilization.

10
MAC
Protocol
for CCRN
Increasing
Spectrum
Utilization
Weighted
Load
Distribution
among
Channels
QoS
Awareness
Increasing
System
Throughput
Decreasing
Channel
Switching
Rate
Interference
Minimization

Development of a strong distribution
mechanism, for the independent channel
selection of heterogeneous CRNs
Limiting the effects of the non-
cooperative mechanism to achieve
fairness
Identification and maintenance of QoS
sensitivity of numerous applications
Development of adaptive and dynamic
medium access mechanism
Maintenance of effective channel usage
through knowledgeable decision
11

• Credit-Token based channel selection [B. Gao, Y. Yang, and J.-M. Park, A credit-token-
based spectrum etiquette framework for coexistence of heterogeneous cognitive radio networks Wireless Communications,
INFOCOM, 2014 Proceedings IEEE]
▫ Enable spectrum sharing among distributed heterogeneous CR
networks with equal priority
▫ Propose a centralized algorithm, not very feasible in heterogeneous
CRN environment.
▫ The medium access and auction policy is not adaptable to
network’s traffic load and traffic’s QoS requirements.
13

• SHARE [Kaigui Bian, Jung-Min , Xiaojiang Du, and Xiaoming Li, and Sung Won Kim. Ecology-inspired coexistence of
heterogeneous wireless networks. Global Communications Conference (GLOBECOM), 2013 IEEE]
▫ Symbiotic Heterogeneous coexistence ARchitecturE (SHARE)
▫ Enable collaborative coexistence of heterogeneous CR networks
over TV white space.
▫ Adopts the symbiotic relationships between heterogeneous
organisms in a stable ecosystem.
▫ Continuous communication imposes higher protocol operation
overhead
▫ Unstable system with no historical prediction
▫ QoS awareness and SU activity on PU arrival are avoided.
14

• FMAC[Yanxiao Zhao; Min Song; ChunSheng Xin, "FMAC: A fair MAC protocol for coexisting
cognitive radio networks," INFOCOM, 2013 Proceedings IEEE , December 2013]
▫ Pioneer on considering coexistence property.
▫ Distributed fair MAC for heterogeneous coexistence.
▫ channel allocation is done without usage pattern prediction,
 SU selects a channel based on only the current sensing result
▫ overall fairness in channel usage is not guaranteed.
▫ QoS awareness is also avoided
▫ The option of channel switching is totally disregarded.
15

16
A SU1, SU2,
SU3 wants to
send packets
SU2 senses two channel
as free, randomly selects
channel 1
SU2 sends
one packet
SU2 keeps sensing and starve
SU1, SU3 senses
two channel as
free, randomly
selects channel 3
Although SU3 has more critical data, it can
randomly select high back-off value than SU2
PU packets
SU4 packets
• Starvation
• Non judicious, random
channel selection
• No QoS awareness
• No usage fairness
• Low throughput
• More interference

17
Credit
Token
SHARE FMAC nQ WF-
MAC
random
WF-MAC
WF-MAC
Distributed x x    
Dynamic x x x (partly) (partly) 
Channel
Selection
(partly) (partly) x  x 
Weighted
Fairness
x (partly) (partly) (partly) (partly) 
Three-state
sensing
x x    
Learning x x  x 
QoS
Awareness
x x x x  

A Distributed Quality of Service Aware Medium Access
in Coexisting Cognitive Radio Networks
19
• Knowledgeable channel selection, and
• QoS aware channel access
Multilevel weighted fair resource utilization
is maintained by the SUs through:
• Channel availability prediction, and
• Channel utility perception
Channel selection stability is achieved
through two dimensional learning:
Provides a rational compensation between
channel sharing and channel switching

• Infrastructure based Coexisting Cognitive Radio Network
• Multiple CRNs
• Two types of users
▫ Primary User (Licensed Users)
▫ Secondary Users (Unlicensed Users)
• | | number of licensed channels
▫ Each channel is conditionally and opportunistically accessible by
the SUs
• Each SU is equipped with two radios
▫ one for spectrum sensing
▫ others is for data transmission
• Each CRN has one Base Station (BS)
▫ Each SU of a specific CRN, share their sensing information
(cooperative sensing) with their BS after a specific time intervals.
20

21
Figure: Coexisting Cognitive Radio Network

22
• A three-state sensing model is used, where each busy state is
further divided into state 1 (accessed by PU) and state 2
(accessed by SU), using a distance based estimation technique.
ss : signal that an SU transmits
sp : signal that the PU transmits,
Si : is the signal that an SU received
ni : is the zero-mean additive white
Gaussian noise (AWGN).
[Y. Zhao, M. Song, C. Xin, and M. Wadhwa, “Spectrum sensing based on three-state model to accomplish all-level fairness for co-
existing multiple cognitive radio networks.” in IEEE INFOCOM, 2012]

23
• SUs exchange control messages over a common control
channel (CCC).
• There are different approaches for transmitting control
messages for CRNs and also for CCC selection, like [12-15].
Figure : Different Types of Packets Transmitted over CCC

24
Table: QoS Aware Traffic Prioritization
[Wi-Fi Alliance. Wi-fi certified for wmm – support for multimedia applications with quality of service in wi-fi networks.
Technical report, Wi-Fi Alliance, 2004]

• In coexisting CRN environment
▫ each CRN functions in a distributive and non-cooperative way.
• Every CRN should work towards maximizing the channel
utilization
▫ which can be achieved only by maximizing individual SUs’
utilization over the course of time.
• With legitimate knowledge of system’s current state will allow
SUs to gain the finest and most rational channel distribution
and maximize spectrum utilization.
28

29
• We are using two dimensional learning mechanism for
channel selection
▫ Perception based learning mechanism
▫ Channel Utility Perception Vector
▫ Arrival probability prediction
▫ Channel availability vector

30
• Whenever an SU has some data to transmit, it sends a RCIV
packet to its BS.
• Then the BS prepares channel information vector
Hi: {0, 1, 2}; Status of channel i
: PU arrival rate over channel i
: SU arrival rate over channel i
: Perception utility of channel i

31
• Using the directives of and , the SU selects a
channel from the channel set for contention
based channel access.
: Perception utility of channel i
: Probability of channel i being free
: Probability that PU will not appear over Channel i
: Probability that SU will not appear over Channel i
: Probability thresholds

33
• The arrival pattern of PU and SU follows possion distribution
• Probability that no SU or PU will appear over the data
transmission time can be calculated
▫ Also the overall probability of a channel being idle.
The expected time
needed to transfer
current packet of the
SU over channel i
The PU arrival rate
over channel i
The SU arrival rate
over channel i
Probability of
channel being free
Channel Availability Vector

• The expected time an SU needs to transmit its current data
packets derived using
▫ maximum achievable data rate βi of each channel
▫ average medium access delay in between two consecutive data
packet transmission,
34
: The length of a single data packet
: The number of packet SU wants to
send
: The expected value of back-off
counter
: The propagation delay
: The length of single time slot

• The BS calculates the maximum achievable data rate βi of each
channel
▫ which is strongly related to the signal-to-noise-ratio
▫ calculated using Shannon’s theorem
35
Bi: Bandwidth of channel i
SINRi : Signal to Interference-plus-Noise-Ratio on SU-BS
transmission link over channel i
Sn, Sk: Received signal power from SU n, k
Np: The noise power
d(n, r), d(k, r): Euclidean distance between the BS and SUs

• We are using a Auto Regression (AR) model of order Δ to
predict the arrival rates of each type of users.
36
The PU
arrival rate
The SU
arrival rate
The autoregressive
coefficient
The prediction
error
[R. A. Yaffee and M. McGee, Introduction to Time Series Analysis and Forecasting: With Applications of SAS and SPSS, 1st ed.
Orlando, FL, USA: Academic Press, Inc., 2000]
[Z. Wen, T. Luo, W. Xiang, S. Majhi, and Y. Ma, “Autoregressive spectrum hole prediction model for cognitive radio systems,” in
Communications Workshops, 2008. ICC Workshops ’08. IEEE International Conference on, Beijing, May 2008, pp. 154–157]

• We have adopted a perception based learning model
which helps the BSs of different CRNs
▫ to build perception about each spectrum bands
 by observing the utility gain and payoffs experienced by
SUs over the course of time.
• Each SU only updates its selected channel’s utility
• BS will aggregate these utilities experienced by several
SUs over different channels.
37

• The set of possible outcomes experienced by SU over a
selected channel is defined as:
38

• uc is the utility perception of channel c, which is
updated after each usage outcomes
39
: The constant gain received
on successful transmission
: The constant payoff on
collision with SU
: The constant payoff for
channel switching

• Every BS initially sets each entry of
a small constant value which
implies no biasness towards any
particular channel.
• After receiving perception utilities
form SUs, respective channels’
perception utilities is updated by
the BS.
40
Weighting factor Number of SUs are
accessing same channel i

44
• QoS-aware Contention Window CWρ selection policy is
shown below
• The back-off counter bρ is selected by taking a random
number between [1, CWρ].
: Number of times the current data packet is
retransmitted because collision with SU or any kind
of bit-error.
: The number of times SU was penalized by PUs

• Secondary users schedule their spectrum usage
in order to maximize spectrum utilization or
throughput.
• To do that, the SUs should have the ability to
rationalize between channel access or switching
in a smart way.
47

• The expected throughput gain of the SU on the time of
channel selection
• Several retransmissions or reappearance will scale down the
expected throughput
• The SU should maintain θC ≥ θth
▫ θth is the throughput threshold
48
: The transmission time over channel
C for η number of packets
: The probability being idle
: Achievable bandwidth of channel c
κ: Constant, value of which is
application dependent
ρ: Traffic priority

• The SU will request for CIV from it’s BS and will select new
channel for transmission
• But it has to define new channel set considering channel
switching cost
49
The channel
switching cost

• We used network simulator version -3 (NS-3)[16][17] as
simulation tool and conduct several experiments for the
performance analysis of our proposed protocol, WF-MAC.
• Compared its performances with
• FMAC[35]
• Two versions of our work:
• random WF-MAC (with random selection of channels)
• nQ WF-MAC (that avoids QoS awareness)
51

• We will use following metrics for evaluating our performance:
▫ Throughput for SUs,
 Calculates the average number of data bits the SUs transmit per
seconds to their BSs over their active periods
▫ Average medium access delay,
 Defines the average time taken for a secondary user to get access
of the medium before transmitting a packet
▫ Protocol operation overhead,
 Measures the amount of control bytes exchanged per successful
data byte transmission, the portion of cost a MAC protocol pays
for each byte of data transmission
▫ Channel selection percentage
 Measures the average percentage of selection from each category
of channel (low, mid, high) over the total simulation period.
54

• We will use following metrics for evaluating our performance:
▫ Integrated performance improvement
 Measures the integrated performance of the studied protocols as
follows,
which quantifies the cost compensation for the increased
throughput and reduced medium access delay performances.
▫ Medium access delay of traffic classes
 Calculate the average medium access delay of each type of traffic
class over the active periods of the SUs.
55

Impact of increasing number of CRNs
57
WF-MAC performs
• 48.98% over the FMAC
• 18.73% over random WF-MAC
• 17.64% nQ WF-MAC protocol
WF-MAC experiences, on an
average, 43.33% less delay than
FMAC

58

59
Weighted
Fair Channel
Selection
QoS Aware
Medium
Access

Impact of increasing number of PUs
61
The gap between the
performance of WF-MAC and
FMAC increases from 33%
to 64%

62
WF-MAC has huge
performance
improvements over
FMAC and random
WF-MAC

63
QoS Aware
Medium
Access

Impact of increasing number of SUs
65
WF-MAC gain higher throughput
• 34% than FMAC,
• 10% random WF-MAC ,
• 16% nQ WF-MAC

66

67
Weighted
Fair Channel
Selection
QoS Aware
Medium
Access

68
[1] D. Goldman, “FCC scrambles to cope with data
avalanche,”
http://money.cnn.com/2011/12/29/technology/whit
espace spectrum/index.html, accessed on May
accessed on May 2015
[2] Facilitating Opportunities for Flexible, Efficient, and
Reliable Spectrum Use Employing Cognitive Radio
Technologies, Federal Communications Commission
(FCC), Washington, D.C. 20554, December 2003
[3] G. M. Peter Steenkiste, Douglas Sicker and D.
Raychaudhuri, “Future directions in cognitive radio
network research,” NSF Workshop, Tech. Rep., 2009
[4] C. R. W. Group, Quantifying the Benefits of Cognitive
Radio, The Software Defined Radio Forum Inc.,
2010, WINNF-09-P-0012-V1.0.0.
[5] W. I. Forum, “Defining cognitive radio (CR) and
dynamic spectrum access (DSA),”
http://www.wirelessinnovation.org/defining cr and
dsa, accessed on April 2015.
[6] S. Pollin, M. Timmers, and L. Van der Perre,
“Emerging standards for smart radios: Enabling
tomorrow’s operation,” in Software Defined Radios,
1st ed., ser. Signals and Communication Technology.
Springer Netherlands, 2011, pp. 11–35
[7] S. D. R. F. Inc., “SDR: Software defined radio,”
https://forums.hak5.org/index. php?/forum/81-sdr-
software-defined-radio , accessed on March 2015
[8] B. Gao, Y. Yang, and J.-M. Park, A credit-token-
based spectrum etiquette framework for coexistence
of heterogeneous cognitive radio networks Wireless
Communications, INFOCOM, 2014 Proceedings
IEEE
[9] Kaigui Bian, Jung-Min , Xiaojiang Du, and Xiaoming
Li, and Sung Won Kim. Ecology-inspired coexistence
of heterogeneous wireless networks. Global
Communications Conference (GLOBECOM), 2013
IEEE
[10] Yanxiao Zhao; Min Song; ChunSheng Xin, "FMAC:
A fair MAC protocol for coexisting cognitive radio
networks," INFOCOM, 2013 Proceedings IEEE ,
December 2013
[11] Y. Zhao, M. Song, C. Xin, and M. Wadhwa,
“Spectrum sensing based on three-state model to
accomplish all-level fairness for co-existing multiple
cognitive radio networks.” in IEEE INFOCOM, 2012
[12] L. Lazos, S. Liu, and M. Krunz, “Spectrum
opportunity-based control channel assignment in
cognitive radio networks,” in Proceedings of the 6th
Annual IEEE Communications Society Conference
on Sensor, Mesh and Ad Hoc Communications and
Networks, ser. SECON’09. Piscataway, NJ, USA:
IEEE Press, 2009, pp. 135–143.
[13] K. Chowdhury and I. Akyldiz, “Ofdm-based
common control channel design for cognitive radio
ad hoc networks,” Mobile Computing, IEEE
Transactions on, vol. 10, no. 2, pp. 228–238, Feb
2011.

69
[14] M. S. Miazi, M. Tabassum, M. Razzaque, and M.
Abdullah-Al-Wadud, “An energyefficient common
control channel selection mechanism for cognitive
radio ad hoc networks,” Annals of
telecommunications, vol. 70, no. 1-2, pp. 11–28,
2015.
[15] Y. Zhang, G. Yu, Q. Li, H. Wang, X. Zhu, and B.
Wang, “Channel-hopping-based communication
rendezvous in cognitive radio networks,”
Networking, IEEE/ACM Transactions on, vol. 22, no.
3, pp. 889–902, June 2014.
[16] “Network simulator-3,” https://www.nsnam.org/,
accessed on: January 2015.
[17] N. Kamoltham, K. Nakorn, and K. Rojviboonchai,
“From NS-2 to NS-3 : Implementation and
evaluation,” in Computing Communications and
Applications Conference (ComComAp), Hong Kong,
January 2012, pp. 35–40.
[18] R. A. Yaffee and M. McGee, Introduction to Time
Series Analysis and Forecasting: With Applications
of SAS and SPSS, 1st ed. Orlando, FL, USA:
Academic Press, Inc., 2000]
[19] Z. Wen, T. Luo, W. Xiang, S. Majhi, and Y. Ma,
“Autoregressive spectrum hole prediction model for
cognitive radio systems,” in Communications
Workshops, 2008. ICC Workshops ’08. IEEE
International Conference on, Beijing, May 2008, pp.
154–157]

[1] ——————–, “QoS Aware Weighted-Fair Medium Access
Control Protocol for Coexisting Cognitive Radio Networks,”
Submitted to EURASIP Journal on Wireless Communications
and Networking, April 2015
[2] ——————–, “Traffic Priority and Load Adaptive MAC
Protocol for Body Sensor Network with QoS Provisioning,”
International Journal of Distributed Sensor Networks on the
issue of “Recent Advances in Energy-Efficient Sensor
Networks (EESN)”, Article ID 205192, 9 pages, vol.2013,
February, 2013 (doi:10.1155/2013/205192)
70

71
• A weighted fair opportunistic medium access control protocol,
WF-MAC, has been developed for QoS aware traffic delivery in
coexisting cognitive radio networks.
• Fully distributed and driven by traffic class priorities and
opportunistic spectrum qualities.
• The two dimensional learning mechanism consisting of
perception learning and channel availability prediction helps our
WF-MAC to achieve as high as 88.56% and 64% improvements
in throughput and medium access delay, respectively, compared
to FMAC for varying arrival rates of primary users.

Channel Selection Stability
74

Channel Selection Stability
75

76
Figure: Distance estimation of three-state sensing model
Three-state sensing model uses two stage decision policy:
• In the first stage, energy detection methodology identify whether a
channel is idle or busy.
• The received signal is then further analyzed based on a distance
based estimation technique, aiming to effectively differentiate PUs
signals from SUs, using the statistical model of the locations of SUs
and known locations of PUs.

• More recent returns have greater weight on the variance.
• Relatively little data needs to be stored
• An exponentially weighted moving average can be defined on
any time series of data.
• The simplest form of exponential smoothing is given by the
formula:
yt = α yt + (1 − α) y(t − 1)
where α is the smoothing factor, and 0 < α < 1.
77

• An AR model expresses a time series as a linear function of its
past values.
• The order of the AR model tells how many lagged past values
are included.
• The simplest AR model is the first-order autoregressive, AR(1),
yt = a1yt-1 + εt
where, yt is the mean-adjusted series in time t,
yt-1 is the series in the previous interval,
{|at| < 1} is the lag-1 autoregressive
coefficient, and
εt is the noise.
78

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