Download Slide 1

A study analysis of Cooperative
spectrum sensing in Cognitive
Radio Networks
Outline of Presentation






Motivation
Objectives
Introduction
Functions
Spectrum Sensing Techniques
Cooperative Spectrum Sensing
 Cluster Based




Simulation Results
Conclusion
Future Work
References
Motivation

Recent measurement by the FCC in the US show 70% of the
allocated spectrum is not utilized
Objectives




To Maximize Probability of Detection
To Minimize the probability of False alarm
To Minimize sensing time
To Maximize throughput
Spectrum Holes
Licensed User Primary User
Unlicensed User  Secondary User
Cognitive Radio

“A cognitive radio (CR) is a radio that can change its
operating parameters dynamically based on interaction
with the environment in which it operates”.

They are designed to provide highly reliable
communication for all the users of the network,
wherever and whenever needed.
Functions of Cognitive Radio
Maximizes throughput
 Mitigates interference
 Facilitates interoperability
 Access secondary markets

Cognitive radio cycle

Cognitive Cycle
Spectrum sensing
Spectrum analysis
Spectrum decision
Design Issues
• Spectrum sensing
Spectrum management
Spectrum sharing
Spectrum mobility
Spectrum Sensing Techniques

The most efficient way of spectrum sensing techniques
are divided into :
Local Spectrum Sensing
Energy detection
Matched Filter detection
Cyclostationary detection

Cooperative Spectrum Sensing
Distributive Cooperative detection
Centralized Cooperative detection
External Cooperative detection
Spectrum Sensing using Energy
Detection method
Block Diagram for Energy Detection Method
Energy detection
•
•
The decision on the occupancy of a band can be
obtained by comparing the decision metric against a
fixed threshold.
Decision metric is given as :-
where
Y[n] – Received signal
N – no of Samples
Binary Hypothesis Testing
H0: x (t) = n(t),
H1: x(t) = s (t) + w (t)
where H0 and H1 are the sensed states for absence and
presence of primary user respectively
The four possible cases for detected signal are :
 Declaring H0 when H0 is true (H0|H0);
 Declaring H1 when H1 is true (H1|H1);
 Declaring H0 when H1 is true (H0|H1);
 Declaring H1 when H0 is true (H1|H0).

Challenges with Energy
detection method
Selection of threshold level for detecting
primary user.
 Inability to differentiate from primary user and
noise.
 Poor performance under low SNR.

Cooperative Sensing
In order to reduce communication overhead, the
users only share the final 1-bit decisions rearding
H0 and H1 rather than the entire decision
statistics.
 Decreases the probabilities of miss-detection
and false alarm considerably.
 Solves hidden primary user problem and it can
decrease sensing time.

Cooperative Sensing
• Uses control channel to share spectrum sensing
result.
• Co-operative sensing is usually performed in two
successive stages: sensing and reporting
• In order to reduce the reporting error, the
cluster based architecture to be used.
Cluster Architecture
ON
ON
ON
ON
Cluster
head
Cluster
head
ON
ON
Cognitive
base station
ON – Ordinary Node
Cluster Based Cooperative sensing
Individual decision will be made by each user
and is send to their cluster head.
• Cluster head makes the local decision and
send to the cognitive Base station(BS)
• Cognitive BS decides the presence or
absence of primary user and broadcasts to
the cluster-heads.
•
Cluster based cooperative sensing
The probabilities of false-alarm and detection for conventional
cooperative scheme are
Qd = 1- (1 - Pd)N
Qf = 1- (1 - Pf)N
• The probability of detection for cluster based cooperative
scheme is
•
where
N= Number of Cooperative users
K= Number of cluster
Pd_i = Probability of detection for ith cluster
Simulation Parameters
No of Users: 100
 No of clusters: 10
 SNR  2 dB
 Pf  0.01

Probability of Detection
Inference: Probability of detection for proposed method is
improved compared to existing method.
Probability of false alarm
0
Probability of false alarm
10
Cluster Method
Conventional Method
-1
10
-2
10
1
2
3
4
5
6
Number of clusters
7
8
9
10
Inference: Probability of false alarm for proposed method is
minimum compared to existing method.
Performance analysis for
Rayleigh Channel
f ( ) 
 
exp  

 
1

,   0


Pd _ R   Pd ( ) f ( )d
0
Pd _ R  e
m 1
V
VT m  2
1  VT   1     2(1T )
1 VT  
2
 e
e 
   


n
!
2

n
!
2
(
1


)
 
n 0 
n 0
 

VT m  2
2
n
Pm _ R  1  Pd _ r
Pm _ R  1  e
VT m  2
2
1  VT   1   

   

n 0 n! 2 
  
n
m 1
 2(1VT ) VT m2 1 VT  
e 2 
e

n 0 n! 2(1   ) 


Comparison of performance of energy
detection in AWGN channel and
Rayleigh fading channel
•
•
We observe that the
performance of energy
detection has degraded when
fading channel is considered.
At Pf=0.1 , the Pm for
Rayleigh fading is high as
compared to that of AWGN
channel.
Comparison of performance of
energy detection in AWGN channel
for different values of SNR


We observe that , the
performance of energy
detection in AWGN channel
increases as SNR increases.
This indicates that the
efficiency of spectrum sensing
can be increased for energy
detection method by
increasing the value of SNR
Cooperative spectrum sensing with
different number of users


We observe that, as no. of
collaborations increases ,the
performance of energy
detection also increases.
When N=5, the performance
in fading channel is much
better than AWGN channel.
Conclusion


Emerging cognitive radio technology has been identified as a
high impact disruptive technology innovation, that could
provide solutions to the “radio traffic jam” problem and
provide a path to scaling wireless systems for the next 25
years.
Efficient spectrum sensing can be achieved by maximizing
the probability of detection.
Future Work


Significant new research is required to address the
technical challenges of cognitive radio networking like
dynamic spectrum allocation methods, spectrum
sensing, cooperative communications, cognitive network
security, cognitive system adaptation algorithms and
emergent system behavior.
A set of cognitive networking testbeds can be
developed that can be used to evaluate cognitive
networks at various stages of their development .
References
•
Mitola , J., and Maguire, G. Q., “Cognitive Radio: Making Software Radios More
Personal”, IEEE Pcrsonal Communications, vol. 6, no. 4, pp. 13-18, August 1999.
•
S. Haykin, “Cognitive radio: brain-empowered wireless communications,” IEEE
J. Select. Area Commun., vol. 23, no. 2, pp. 201–220, Feb. 2005.
•
C. Sun, W. Zhang and K.B. Letaief, ”Cluster-based cooperative spectrum
sensing in cognitive radio systems”. in Proc. IEEE ICC2007, pp. 2511- 2515,
June, 2007
•
Zhai Xuping and Pan Jianguo, “Energy-Detection Based Spectrum Sensing for
Cognitive Radio”, IET Conference on Wireless, Mobile and Sensor Networks, 2007.
(CCWMSN07), pp:944 – 947, 2007.
References(contd.)
•
Unnikrishnan, J. , Veeravalli,V.V. , “Cooperative Sensing for Primary Detection in
Cognitive Radio”, IEEE Journal, pp. 18‐27, Feb 2008.
•
Junyang Shen, Tao Jiang, Siyang Liu and Zhongshan Zhang, “Maximum Channel
Throughput via Cooperative Spectrum Sensing in Cognitive Radio Networks,”
IEEE transactions on wireless communications, vol. 8, no. 10, October 2009.
•
Guo, Peng, Shaovi. Haiming and Wenbo “ Cooperative Spectrum Sensing with
Cluster-based Architecture in cognitive Radio Networks, “ Wireless Signal Processing
and Network Lab, University of Posts and Telecommunications, Beijing,
China, 2009.
Thank you !