Spectrum Sensing in Emergency Cognitive Radio Ad Hoc Networks (CRAHNs) : A Multi-Layer Approach

Sasirekha GVK, ,Supervisor: Prof. Jyotsna Bapat, IIIT Bangalore

Requirements of Emergency CRAHNs:

•Accuracy

•Resource efficiency

•Low latency in the delivery of packets,

•Adaptive to varying number of SUs,

•Adaptive to varying SNR conditions,

•Uniform battery consumption

•Resilience to Byzantine attacks

SNR

Threshold

Sensing Mechanism

Local decisions, accuracy

,

Fusion Rule

NumberOf

SensingSUs

Sensing timeFrequency

of sensing

PHY LINK

Global decisions, accuracy

,

Performance

Literature surveyCollaborative spectrum sensing

1. Amir Ghasemi and Elvino S. Sousa,

2. Wei Zhang, Rajan K. Mallik, Khaled Ben Letaief

3.Clancy

4. L. Chen, J. Wang, S. Li,

5. Yunfei Chen

Static/Reactive methods using ‘OR’ based fusion, Civilian Networks

Considering only some parameters for optimization

Cognitive Radio Ad hoc Networks

Ian F. Akyildiz, Won-Yeol Lee, Kaushik R. Chowdhury, Protocol stack, routing, transport and high level architecture

Emergency NetworksAdaptive Ad-hoc Free Band Wireless Communications Requirements in

general

IEEE Standards IEEE 802.22 (Shell Hammer) Regional Area Networks in TV band

Our proposal proactive, dynamic, LRT based (better immunity against Byzantine attacks) meeting sensing requirements for emergency networks

Multi-Layer Framework

Focus of the research

Confidence

Link Layer

Blind/Semi-blindSpectrum Sensing

Averaging AndFinal

DecisionLogic

Decision

Rx_Signal

Threshold

Data Fusionwith opt. KEstimator

Soft/Hard Decision from other users

Cognitive Radio Receiver

Front End

Physical Layer

Adaptive Thresholding

Group Decision

Sensing Scheduler

Being a Multi-Layer Multi-Parameter optimization problem tackled as 2 levels•Level 1: Local Optimization: Spectrum sensing method, time, frequency•Level 2: Global Optimization: Data Fusion, Optimal number of Sensing CRs•Cross Layer: Adaptation of local sensing threshold based on Global Decisions

Results• Estimation of smallest number of sensing CRs for a targeted accuracy.

• Algorithm for adapting the number of sensing SUs in changing environments; i.e. network size and SNR. Proposed for centralized and distributed spectrum sensing.

• Algorithm for adapting threshold for local energy detection based on global group decisions.

• Application of evolutionary game theory for behavioral modeling of the network.

0.9 0.91 0.92 0.93 0.94 0.95 0.96 0.97 0.98 0.99 10.85

0.9

0.95

1

1.05

Qd desired

Qd

actu

al

Qd actual versus Qd desired for various sensitivites

reference

-3%+3%

(Pd,Pf)=0.4,0.1

(Pd,Pf)=0.5,0.15

(Pd,Pf)=0.6,0.25(Pd,Pf)=0.76,0.4

(Pd,Pf)=0.85,0.5 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2

x 104

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

iterations

Variance of energy spent,Payoff Qd, probability of sense of an SU with Qd target=0.9, Group SNR 0dB, Event 2 of Table I at iterations=10000

Norm

aliz

ed v

alu

e o

f variance /

pro

babili

ty

Normalized variance of energy spent across SUs

Probability of detect of fused dataProbability of sense of an SU

Sample Results on the Estimation of minimal no. of CRS and Adaptation of CRs

Future WorkLateral Application Areas

Cloud Networking Smart Grids

Open Issues

Cognitive Radio Ad hoc Network

Time synchronization

Optimized Link State Routing

Co-operative Spectrum Sensing

Com

mon C

ontrol Channel

Spectrum Allocation

Security •Provision of Common Control Channel

•Integration of all the layers

•Security Related Issues•Byzantine attacks•Primary User Emulation Attacks•Trustworthiness/ Authentication

Back up slides

SU

SU

SUSU

Coordinator

Centralized Architecture

SU

SU

SU

SU

SU

Distributed Architecture

Cognitive Radios : Secondary Users (SUs)Dynamic Spectrum Access

•Spectrum Sensing Local & Collaborative •Spectrum Allocation•Spectrum Mobility

Application Scenarios

PU

[f1 f2][f3 f4 f5 f6]

[fr-2 fr-1]

[fr]

Mobile CRAHNScenario model

PU PU

PU

•Military Networks•Disaster Management

Features:• Nomadic Mobility• Group Signal to Noise Ratio• Collaborative Spectrum Sensing

PHY LINK Performance Metrics

SNR

Threshold

Sensing Mechanism

ChannelModel

Local decisions,

Pdi

, Pfi

Fusion Rule

NumberOf

SensingSUs

Risk

From ith SU

From other (K-1) SUs

PUUsage pattern

Level 1 OptimizationLevel 2 Optimization

Sensing time

Frequency of sensing

Qdk

Qfk

Ik

k F fk D dkR C Q C Q C

k kI 1 R

k k k

k

J αI 1 α η

N k0 α 1,η

N

Two levels of optimization

Confidence

)λ(Yβ-ttt

tte1

1λYfz

t

2t

t1t λ

eEμλλ

)z1(zeμ2λλ tttt1t

Adaptive Threshold

Adaptive Threshold based on Group Decisions

)P,P,k(fQ~

f

~

dd

QQ kminK desired_dd

0.9 0.91 0.92 0.93 0.94 0.95 0.96 0.97 0.98 0.99 10.85

0.9

0.95

1

1.05

Qd desired

Qd

actu

al

Qd actual versus Qd desired for various sensitivites

reference

-3%+3%

(Pd,Pf)=0.4,0.1

(Pd,Pf)=0.5,0.15

(Pd,Pf)=0.6,0.25(Pd,Pf)=0.76,0.4

(Pd,Pf)=0.85,0.5

Group SNR-> Pd_av, Pf_av-> K

Estimation of optimal number of CRs required for sensing for targeted accuracy

Behavioral ModelInteraction between autonomous CRs modeled

using game theory

PoliciesFrequencies to sense

Who should be the coordinator? Authenticate the entry into network

Implementation (Protocols)Adaptive System Design

Levels Of Abstraction

Ref: http: //www.ir.bbn.com/~ramanath/pdf/rfc-vision.pdf

Approaches of Analysis (Our Contributions)• Iterative Game (pot luck party) ---- Penalty• Evolutionary Game based on Replicator Dynamics --- Reward• Public Good Game ---Reward

• How many should sense? ---- K• Who should sense?• Assuming proactive spectrum sensing in the period quiet period

Game theoretical modeling

Adaptive Proactive Implementation Model: Centralized Architecture

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2

x 104

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

iterations

Variance of energy spent,Payoff Qd, probability of sense of an SU with Qd target=0.9, Group SNR 0dB, Event 2 of Table I at iterations=10000

Nor

mal

ized

val

ue o

f var

ianc

e / p

roba

bilit

y

Normalized variance of energy spent across SUs

Probability of detect of fused dataProbability of sense of an SU

s avP _Ps _ av s _ avJ α I 1 α 1 P

Utility Function

Decentralized Architecture

)k(J)k(MaxK

1 constant a is ε where

εJ)k(MinK '

J (1 ) I

1J C Q C Q

2 ND d F f

0 10 20 30 40 50 60 70 8010

0

101

102

103

104

105

N

No. o

f Multi

plicatio

ns

Computational Complexity Vs. N

Classical Iterative Algorithm

Proposed Algorithm

1. Sasirekha GVK, Jyotsna Bapat, “ Adaptive Model based on Proactive Spectrum Sensing for Emergency Cognitive Ad hoc Networks”, CROWNCOM 2012, Stockholm, Sweden

2. Sasirekha GVK, Jyotsna Bapat , “Optimal Number of Sensors in Energy Efficient Distributed Spectrum Sensing”, CogART 2010. 3rd International Workshop on Cognitive Radio and Advanced Spectrum Management. In conjunction with ISABEL 2010. November 08-10, 2010, ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5702906

3. Sasirekha GVK, Jyotsna Bapat, “Optimal Spectrum Sensing in Cognitive Adhoc Networks: A Multi-Layer Frame Work”,

CogART 2011 Proceedings of the 4th International Conference on Cognitive Radio and Advanced Spectrum Management

Article No. 31, ACM,  ISBN: 978-1-4503-0912-7 doi>10.1145/2093256.20932874. Sasirekha GVK and Jyotsna Bapat, “Evolutionary Game Theory based Collaborative Sensing Model in Emergency

CRAHNs," Journal of Electrical and Computer Engineering, Hindawi Publishing Corporation, Special issue "Advances in Cognitive Radio Ad Hoc Networks“, (accepted)

5. Sasirekha GVK ,George Mathew Tharakan, Jyotsna Bapat, “Energy Control Game Model for Dynamic Spectrum Scanning”, IJAACS, Inderscience, 2012, DOI: 10.1504/IJAACS.2012.046280

6. Sasirekha GVK, Jyotsna Bapat, “Cognitive Radios: A Technology for 4G Mobile Terminals”, Third Innovative Conference on Embedded Systems, Mobile Communication and Computing, 11th- 14th August, 2008, Infosys, Mysore, India, http://www.pes.edu/mcnc/icemc2/

7. Rajagopal Sreenivasan, Sasirekha GVK and Jyotsna Bapat, “Adaptive Threshold based on Group Decisions for Distributed Spectrum Sensing in Cognitive Adhoc Networks”, Wimone 2010 8. Rajagopal Sreenivasan, Sasirekha GVK and Jyotsna Bapat, “Adaptive Threshold based on Group intelligence”,

International Journal of Computer Networks and Communications , AIRCC,May 20119. Sasirekha GVK, Jyotsna Bapat IGI-CRN Book Chapter # 4: “Spectrum Sensing in Emergency Cognitive Radio Ad Hoc

Networks”, Cognitive Radio Technology Applications for Wireless and Mobile Ad hoc Networks. IGI Global (under (under review)review)

Papers Published on Research Topic