Neural networkx - Pham Chuan

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Oct 20, 2013 (3 years and 7 months ago)

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A NEURAL NETWORK BASED SPEDTRUM
PREDICTION SCHEME FOR COGNITIVE RADIO



Networking Lab

Presenter:
CHUAN PHAM

Date: 2013/06/15

Vamsi

Krishna
Tumuluru
, Ping Wang and
Dusit

Niyato

Center for Multimedia and Network Technology (
CeMNeT
)

School of Computer Engineering,
Nanyang

Technological University, Singapore
.

IEEE Communications Society 2010

1

OUTLINE


Introduction


Spectrum prediction using Neural Network


Simulation and analysis


Conclusions

2

Introduction


To minimize the
interference with the
primary users, the
secondary users need a reliable spectrum sensing mechanism

sensing full spectrum.


Hardware constrains


sense only a part of the spectrum


To let the secondary users efficiently manage the sensing
mechanism, channel status prediction becomes important.


The secondary users may predict the status of a channel based
on the sensing history and sense only if a channel is predicted
to be idle in the next time slot




save sensing energy, effective bandwidth




3

idle

Prediction channel status

4

1

-
1



1

1

1

1

-
1

-
1

-
1



1

㼿?

t
-
r+1

t
-
r+2


t
-
1


t

History :r slots


Current time

Using Neural network to pr
ediction status of next slot

busy

Spectrum prediction using neural network


MLP Predictor Design

5


Generated for the channel
by sensing the channel

status at every slot for a

duration T (series of
-
1,1)

MLP Predictor Training

6


Input vector:


Output desired value: x
t+1


Output value:

1

-
1



1

1

1

1

-
1

-
1

-
1



1

t
-
r+1

t
-
r+2


t
-
1


t

Duration time T

r

=1 busy

=
-
1 idle

Forward Prediction Process


7

Bi
-
polar sigmoid function

Backward Process

8


Update weight:

Learning rate

Momentum


Error e
t
:


Square error
creterion
:

Simulation and analysis


Neural network: 2 hidden layers (the first hidden
layer: 15neurals, the second hidden layer: 20
neurons)


Learning rate: (0,1)


Momentum:[0.5,0.9]


The length of the training and testing data: 1000
and 30000 slots


Evaluate base on wrong prediction probability

9

Simulation and analysis

10


Traffic intensity


Simulation with interval time 6000 slots and 500 epochs: wrong prediction 6.31%

Conclusion


Channel status prediction in cognitive radio
network



Save sensing energy greatly



Improve spectrum utilization


The author didn’t mention about choosing the
number of neural in input layer



11


THANK YOU

References


[1] Federal Communications Commission, “Spectrum policy task force,” in
Rep. OET Docket No. 02
-
135, Nov.2002.


[2] Federal Communications Commission, “Evaluation of performance of prototype TV
-
band white space devices Phase II,” in
Rep. OET
DocketNo
. 08
-
TR
-
1005, Oct. 2008.


[3] S.
Haykin
, “Cognitive radio: Brain
-
empowered wireless communications,”
IEEE Journal on Selected Areas in
Communications, vol. 23,
no. 2, pp. 210

230, Feb. 2005.


[4] T. V. Krishna and A. Das, “A survey on MAC protocols in OSA networks,”
Computer Networks, vol. 53, no. 9, pp. 1377

1394, 2009.


[5] N. Shah, T.
Kamakaris
, U.
Tureli

and M.
Buddhikot
, “Wideband spectrum sensing probe for distributed measurements in
cellular band,” in
Proceedings of ACM International Workshop on Technology and Policy for Accessing Spectrum, vol. 222, Aug.
2006.


[6] Y.
Hur
, J. Park, et al., “A wide band analog multi
-
resolution spectrum sensing (MRSS) technique for cognitive radio (CR)
systems,” in
Proceedings of IEEE International Symposium on Circuits and Systems,
ISCAS 2006, May 2006.


[7] Z.
Tian

and G. B.
Giannakis
, “A wavelet approach to wideband spectrum sensing for cognitive radios,” in
Proceedings of
International Conference on Cognitive Radio Oriented Wireless Networks and Communications (CROWNCOM), 2006, pp. 1

5,
June 2006.


[8] S.
Haykin
,
Neural Networks: A comprehensive foundation, 2nd ed.,
Prentice Hall, pp. 161

175, 1999.


[9] S.
Yarkan

and H.
Arslan

H, “Binary time series approach to spectrum prediction for cognitive radios,” in
Proceedings of IEEE
Conference on Vehicular Technology (VTC), pp. 1563

1567, Sept. 2007.


[10] I. A. Akbar and W. H. Tranter, “Dynamic spectrum allocation in cognitive radio using hidden
markov

models: Poisson
distributed case,” in
Proceedings of IEEE
SoutheastCon
, pp. 196

201, March 2007.


[11] C. H. Park, S. W. Kim, S. M. Lim and M. S. Song, “HMM based channel status predictor for cognitive radio,” in
Proceedings
of Asia
-

Pacific Microwave Conference (APMC), pp. 1

4, Dec. 2007.


[12] K. S.
Narendra

and K.
Parthasarathy
, “Identification and control of dynamical systems using neural networks,”
IEEE
Transactions on Neural Networks, vol. 1, no. 1, pp. 4

27, March 1990.

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