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COGNITIVE RADIO FOR NEXTGENERATION WIRELESS NETWORKS:
AN APPROACH TO OPPORTUNISTIC CHANNEL
SELECTION IN IEEE 802.11-BASED WIRELESS
MESH
Dusit Niyato, Nanyang Technological University
Ekram Hossain, University of Manitoba
IEEE Wireless Communication Feb. 2009
Outline
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

Introduction
Cognitive Radio




An Approach to Opportunistic Channel Selection in IEEE
802.11-Based Wireless Mesh
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



Basic Components, Approaches
In Different Wireless Systems
Research Issues in Protocol Design
System Model
Dynamic Opportunistic Channel Selection Scheme
Performance Evaluation
Conclusion
Comments
Introduction
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
Frequency spectrum is the scarcest resource for wireless
communications


Software radio



may become congested to accommodate diverse types of air
interfaces in next-generation wireless networks
Improves the capability of a wireless transceiver by using
embedded software
Enable the radio transceiver to operate in multiple frequency
bands
Cognitive radio


A special type of software defined radio
Able to intelligently adapt itself to the changing environment
Cognitive Radio
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
Basic Components

Observation Process
Measurement and noise reduction mechanism
 Passive observation
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

Active observation


The radio transceiver silently listens to the environment.
Special messages or signals are transmitted and measured to obtain
information about the surrounding environment
Learning Process
Extract useful information from collected data
 Reinforcement learning algorithm



is used when the correct solution is unknown
Learning through interactions
Cognitive Radio (cont’d)
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 Planning
and Decision Making Process
 Using
knowledge obtained from learning to schedule and
prepare for the next transmission
 A transceiver must decide to choose the best strategy to
achieve the target objective
 Action
 The
action of a transceiver is controlled by the planning and
decision making process
Cognitive Radio (cont’d)
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
Approaches

Estimation Technique

Obtain information about the ambient network environment
Game Theory
 Evolutionary Computation


Genetic algorithm
Fuzzy Logic
 Markov Decision Process
 Pricing Theory
 Theory of Social Science
 Reinforcement Learning

Cognitive Radio (cont’d)
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
In different wireless systems
 IEEE
802.11 and 802.16 Networks
 May
operate in the same unlicensed frequency band
 Efficient spectrum management and planning are required
 IEEE
802.22 Networks (WRANs)
 The
first wireless communication standard adopting
intelligent software defined radio
 Ultra
Wideband-based (WPANs)
 Cooperative Diversity Wireless Networks
 Primary
users and secondary users
Cognitive Radio (cont’d)
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
Research issues in protocol design
 Lightweight
and cooperative protocols for cognitive
radio networks
 Battery-limited,
energy consumption for the execution of
estimation, learning, and decision making algorithm should
be minimized
 Cross-layer
 To
optimization in cognitive radio networks
optimize QoS performance in a cognitive radio network
Dynamic Channel Selection Scheme
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
In the proposed scheme
A
wireless node/mesh client learns physical (i.e., signal
strength) and MAC layer (i.e., collision probability)
 Accordingly selects the best channel to connect to a
mesh router
 The decision can be made independently in each node
in a distributed manner by using an intelligent algorithm
Dynamic Channel Selection Scheme (cont’d)
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
System Model
 IEEE
802.11 Mesh
 100m*100m
 No centralized controller
Dynamic Channel Selection Scheme (cont’d)
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Fuzzy logic controller
Pc(f): collision probability on channel f  estimate the amount of traffic load
γf: estimated signal strength
Dynamic Channel Selection Scheme (cont’d)
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
Wireless node utility
 The
decision on dynamic channel selection at each node
is based on utility function of collision probability Pc(f)
and received signal strength γf on channel f.
 Both collision probability and received signal strength
impact the throughput and error performances
experienced by a wireless node.
Dynamic Channel Selection Scheme (cont’d)
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Fuzzy logic
 Use
“collision probability” as an indicator of traffic
load in each channel
 The interference rules are used to gain information on
the traffic load condition in a channel
Estimated collision prob.
Example:
Result utility
Dynamic Channel Selection Scheme (cont’d)
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Let mf,i denote the membership function for channel
f obtained from fuzzification.
This mf,i can be obtained using a standard
fuzzification method.
Then the fitness of rule k to the traffic load
F
condition can be obtained from M k   f 1m f ,i
The estimated utility
The normalized fitness
Dynamic Channel Selection Scheme (cont’d)
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
Learning algorithm
 is
used to approximate the utility Ui,f,k perceived by
each wireless node corresponding to the different
traffic load condition in the service area
α: the learning rate
Uoldi,f,k: the utility of the previous learning iteration
Dynamic Channel Selection Scheme (cont’d)
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
Decision on Channel Selection
 Wireless
node i chooses channel that provides the
highest Ui,f
 This channel selection is executed periodically.
 The decision can be made if the estimated collision
probability and received signal strength change by an
amount larger than the predefined thresholds,
 which
implies that one or more new nodes are accessing the
channel and/or some nodes have terminated connections
with the corresponding mesh router.
Dynamic Channel Selection Scheme (cont’d)
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Performance Evaluation
 Each
router operates in DCF mode
 For the channel selection scheme we set α: 0.1, and it is
executed at each node periodically every 2 min.
 Using MATLAB to run the time-driven simulation
Dynamic Channel Selection Scheme (cont’d)
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Wireless nodes and the associated mesh routers: a) at time 0; b) after 30 minutes
Dynamic Channel Selection Scheme (cont’d)
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a) Wireless nodes and the associated mesh routers (for non-uniform node distribution)
b) Variation in average node throughput
Dynamic Channel Selection Scheme (cont’d)
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Effect of uniformity of node distribution on the network utility
Conclusion
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
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An overview of the difference components in
cognitive radio and the related approaches have
been presented
The dynamic channel selection for opportunistic
spectrum access in IEEE 802.11-based multichannel
wireless mesh networks
It performs significantly better than some of the
traditional schemes, especially with non-uniform
node distribution in the service area.
Comments
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
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Provide an introduction to cognitive radio’s
approaches.
Learning rate selection is a issue.
 Convergence
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and performance
Comparison with other channel selection scheme?