Cognitive radio network_MS_defense_presentation

Quality of Service Aware Medium Access in Coexisting Cognitive

Radio Networks

Student:Iffat AnjumRoll: RK-554MS 3rd Semester

Thesis Supervisor:Md. Abdur RazzaqueProfessor

May 2015Department of Computer Science and Engineering

University of Dhaka

Presentation on MS Thesis Defense

2ContentsIntroductionChallenges

Design GoalsRelated WorkContributionSystem Model

Proposed WF-MACPerformance Analysis

ReferencesConclusion

3Introduction

4Introduction

[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]

http://money.cnn.com/2011/12/29/technology/whitespace%20spectrum/index.htm

5Introduction• 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

Figu

re :

Cog

nitiv

e R

adio

Net

wor

k

Figure: Opportunistic Spectrum Usage

6Introduction

Figure : Performance Improvements Achieved by CR

7

• 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.

Introduction

[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]

https://forums.hak5.org/index

8Introduction

Cognitive radio networks are expected to be ubiquitous and multiple CRNs often coexist with each other

Figure: Coexistence of Cognitive Radio Networks

9Motivation• 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

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.

10Design Goals

MAC Protoco

l for CCRN

Increasing Spectrum Utilization

Weighted Load

Distribution among

Channels

QoS Awareness

Increasing System

Throughput

Decreasing Channel

Switching Rate

InterferenceMinimization

11Challenges

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

12

State-of-the-art Works

13Related Work• 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.

14Related Work• 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.

15

•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.

Related Work

16Related Work

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 packetsSU4 packets

• Starvation• Non judicious, random channel selection• No QoS awareness• No usage fairness• Low throughput• More interference

17Related WorkCredit 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

18

Weighted Fair Medium Access Control (WF-MAC)

19ContributionA Distributed Quality of Service Aware Medium Access in Coexisting Cognitive Radio Networks

Multilevel weighted fair resource utilization is maintained by the SUs through:

• Knowledgeable channel selection, and• QoS aware channel access

Channel selection stability is achieved through two dimensional learning:

• Channel availability prediction, and• Channel utility perception

Provides a rational compensation between channel sharing and channel switching

20System Architecture• 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.

21System Architecture

Figure: Coexisting Cognitive Radio Network

22Three State Sensing Model

• 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 transmitssp : 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]

23Control Packets• 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]

Traffic Prioritization

25

Not

atio

n Ta

ble

26W

F-M

AC D

esig

n Co

mpo

nent

s

27

Weighted Fair Channel Selection

28Weighted Fair Channel Selection

• 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.

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


• 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


33Channel Availability Prediction• 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 iThe SU arrival rate over channel iProbability of channel being free

Channel Availability Vector

34

• 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,

Channel Availability Prediction

: 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

35

• 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

Channel Availability Prediction

Bi: Bandwidth of channel iSINRi : Signal to Interference-plus-Noise-Ratio on SU-BS transmission link over channel iSn, Sk: Received signal power from SU n, kNp: The noise powerd(n, r), d(k, r): Euclidean distance between the BS and SUs

36

Channel Availability Prediction• We are using a Auto Regression (AR) model of order Δ

to predict the arrival rates of each type of users.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]

37

Channel Utility Perception

• 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.

38

Channel Utility Perception•The set of possible outcomes experienced by

SU over a selected channel is defined as:

39

•uc is the utility perception of channel c, which is updated after each usage outcomes


: The constant gain received on successful transmission : The constant payoff on collision with SU : The constant payoff for channel switching

40

• 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.


Weighting factor

Number of SUs are accessing same channel i


42

QoS Aware Medium Access Control

43QoS Aware Medium Access

44

QoS Aware Medium Access• 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

45Q

oS A

war

e M

ediu

m

Acce

ss

46

Channel Switching Mechanism

47

Channel Switching Mechanism

• 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.

48Rationalization of Channel Switching

• 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

: 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 dependentρ: Traffic priority

49Rationalization of Channel Switching• 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

The channel switching cost

50

Performance Evaluation

51Simulation Environment

• 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)

52

Sim

ulat

ion

Para

met

ers

53Random Scenario

54

• 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.

Performance Metrics

55

• 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.

Performance Metrics

56

Impact of Increasing Number of CRNs

57

Impact of increasing number of CRNs

Simulation Result

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


Simulation Result

Additional load of RCIV (20

bytes) and CIV (260 bytes)

notably increase (42% -43.2%)

the protocol operation overhead

of WF-MAC

With the increasing CRN, FMAC

decreases its integrated

performance greatly, 72.13%

than WF-MAC

59


Simulation ResultWeighted

Fair Channel Selection

QoS Aware Medium Access

60

Impact of Increasing Number of PUs

61

Impact of increasing number of PUs

Simulation Result

FMAC earns 26.84%

less throughput

than WF-MAC

WF-MAC obtains SU

throughput

• 41.65% than nQ WF-MAC,

• 68.65% than random WF-

MAC,• 88.56% than FAMC

The gap between the performance of WF-MAC and FMAC increases from 33%to 64%

62


Simulation Result

WF-MAC has huge

performance improvements

over FMAC and random WF-MAC

Almost double (48.49%)

63


Simulation Result


64

Impact of Increasing Number of SUs

65

Impact of increasing number of SUs

Simulation Result

WF-MAC gain higher throughput • 34% than FMAC, • 10% random WF-MAC ,• 16% nQ WF-MAC

66


Simulation Result

67


Simulation Result

Weighted Fair

Channel Selection


68References[1] D. Goldman, “FCC scrambles to cope with data

avalanche,” http://money.cnn.com/2011/12/29/technology/whitespace 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.

69References[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]

70

List Of Publications

[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)

71

Conclusion• 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.

Thank You

Any Questions ?

73

Channel Selection Stability

74


Simulation Result

75


Simulation Result

76

Three-State Sensing Model

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.

77Exponentially Weighted Moving Average• 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.

78Auto-Regression (AR) Model• 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.

79

AR vs EWMA

80

Education

Cognitive radio network_MS_defense_presentation