COGNITIVE RADIO NETWORKING AND RENDEZVOUS

Presented by Vinay chekuri

UHCL

Contents

1. Introduction 2.Wireless networking challenges3. Cognitive network4.Autocratic method of cognitive radio network

development5. Waveform distribution and Rendezvous6.Distributed AI7.Cognitive radio network Test beds8.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

SpectrumCoordination

Future Internet

Supernode(mobile or fixed)

Cognitive Radio Nodes

The Global Control Plane and Architecture Internetworking

Autoconfiguration and Bootstrapping Protocols

PHY Adaptation

Flexible MAC Layer

Network Layer

Protocols

Name & Service DiscoveryCross-Layer Aware Routing

Forwarding IncentivesNetwork Management Architecture

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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