COGNITIVE RADIO NETWORKING AND RENDEZVOUS

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23 Οκτ 2013 (πριν από 3 χρόνια και 9 μήνες)

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COGNITIVE RADIO NETWORKING
AND RENDEZVOUS




Presented by


Vinay

chekuri




Contents

1. Introduction

2.Wireless networking challenges

3. Cognitive network

4.Autocratic method of cognitive radio network
development

5. Waveform distribution and Rendezvous

6.Distributed AI

7.Cognitive radio network Test beds

8.Conclusion

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INTRODUCTION


Wireless technology is rapidly proliferating into all aspects of computing
and communication.


Radio technology will be at the very heart of the future computing world.


Cognitive radios offer the promise of being just this disruptive technology
innovation that will enable the future wireless world.


Cognitive radios are fully programmable wireless devices that can sense
their environment and dynamically adapt their transmission waveform,
channel access method, spectrum use and networking protocols as
needed for good network and application performance


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Wireless Networking Challenges

Why is wireless networking hard?



Resources are constrained


Spectrum “scarcity”


bandwidth & delay issues


Environment changes


Mobility


different surroundings (indoor, urban, rural)


Varying physical properties


Wireless communication path changes over time


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Cognitive network


Basic functionality of the cognitive radio system is the ability to transfer
the information and solutions among the nodes operating on the network.


Cognitive network is more than a network of cognitive radios.


Cognitive network exhibits distributed intelligence by configuring
individual nodes to meet dynamic set of network goals.


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Cognitive networking vision

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Autocratic method of cognitive radio
network development


In this method one radio develops a waveform and pushes it out to the
other nodes for them to use.


This method falls short of realizing the full potential of cognitive radio
network.


It is because one radios optimized waveform may not be the same as
another.

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Waveform distribution and
Rendezvous


Simplest approach to enabling communication among cognitive radio
nodes is through a static control channel.


This model uses two scenarios


1. in band signaling


2. out
-
of
-
band signaling


Rendezvous


The method by which a radio hails and enters a network.






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Problems associated with static control channel
and methods to overcome



Static control channels which are easily implemented are problematic because
they are easily jammed and rendered useless.


In order to overcome this few proposals have been mad e which include


1. Using dynamic control channels.


2. Remove control channel and use physical layer descriptors.


3. Use of embedded cyclostationary signatures in OFDM based systems.


4. Transmitting a beaconing signal

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Cognitive radio networks


Cognitive networks uses objective functions that optimize with respect to
network performance.


They use game theory approach to optimize an ad hoc network with
respect to power and channel control.


Game theory has been widely studied for wireless network optimization to
look for optimal states for all zones

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Distributed AI


Distributed AI offers significant potential to improve the global solutions
and reduces the time and power required by any individual node.


Benefit from looking at the whole network instead of single node adaption
is the advantage of available processing power capabilities of each node.


Genetic algorithms have shown themselves to be easily separable for
processing portions on different processors.


Goldberg cites many methods that take advantage of the population of a
GA in a distributed sense.

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Popular technique is to create islands of population.


These are then optimized.


Parallel GA’s have some form of migration or sharing of population.


Implementation of the migration should be designed to consider the
required network overhead.

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Cognitive radio wireless network
testbeds


Controlled
testbeds

that can be used for relatively early testing of
prototypes of partially or fully integrated networks.


Key requirements are


flexibility



high degree of control



isolation,
andrepeatability

and


safety (i.e., errors in


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Open
testbeds

that can support larger scale experiments in fully realistic
environments.


The key difference with controlled
testbeds

is that being immersed in the
real world (“open”), the signal propagation environment will include the
effects of real world objects, mobile objects and people, and possibly
interference from a variety of RF sources.



Key requirements include heterogeneity and programmability at all
levels of the system.

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Cognitive radio test bed deployment plan

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Conclusion


A network of cognitive radio must include methods by which to transfer
waveforms among all nodes.


Take into consideration the needs of all other nodes when designing a new
waveform.


Consideration should be given to the overhead required on the network to
transfer the information related to the cognitive radio performance and
network maintenance.

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References

(
1
)

C
.

Cordeiro

and

k
.
challapali

cognitive

protocal

for

multichannel

wireless

networks
.

(
2
)

J
.
zhao,h
.
zheng

“distributed

coordination

in

dynamic

spectrum

allocation

networks”
.

(
3
)

J
.
neel
,

“Analysis

and

design

of

cognitive

radio

networks

and

distributed

radio

resource

management

algorithms”
.

(
3
)

Genetic

algorithms

in

search

,optimization

and

machine

learning

by

D
.
E
.
Goldberg
.

(
4
)

“A

survey

of

parallel

distributed

genetic

algorithms”

by

E
.
Alba



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