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Implementation of a genetic algorithm-based
decision making framework for opportunistic
radio
Authors: Soamsiri Chantaraskul, Klaus Moessner
Source: IET Commun., Vol.4, No.5, 2010, pp.495 - 506
Presenter:Ya-Ping Hu
Date: 2011/12/23
Outline
 Introduction
 Decision making framework for the OR
 GA approach for the decision making engine
 Development of demonstration platform for the decision
making engine
 Test cases and engine performance observation
 Conclusion
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Outline
 Introduction
 Decision making framework for the OR
 GA approach for the decision making engine
 Development of demonstration platform for the decision
making engine
 Test cases and engine performance observation
 Conclusion
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Introduction
 Innovation of the cognitive radio (CR) concept with the
aim to enhance SDRs by exploiting the environmental
awareness and intelligent adaptation
 The opportunistic radio (OR) is proposed, which only
confines its knowledge to the spectrum awareness
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Introduction (cont.)
 Two major aspects of the OR technology
 Determine the best opportunity
 Define an optimum usage of such opportunity
 Soft-computing methods used in the proposed
framework
 Rule-based reasoning and case-based reasoning
 Genetic algorithm
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Outline
 Introduction
 Decision making framework for the OR
 GA approach for the decision making engine
 Development of demonstration platform for the decision
making engine
 Test cases and engine performance observation
 Conclusion
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Decision making framework for OR
 Key components and their relationships
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Decision making engine
 Filtering engine
 Take into account the context information, the related policies
and profiles
 Discards ineffective solutions
 Filters the available solutions
 Provides a smaller range of possible set of configurations
 Reasoning engine
 The operational configuration is taken into account
 The preferred solution is obtained
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Filtering mechanism
 Concerned value (CV) algorithm
 Collaborative filtering or social filtering
 Case-based reasoning (CBR)
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Reasoning engine
 The operational configuration is taken into account and
the preferred solution is obtained
 The approach is based on the GA
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Outline
 Introduction
 Decision making framework for the OR
 GA approach for the decision making engine
 Development of demonstration platform for the decision
making engine
 Test cases and engine performance observation
 Conclusion
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Genetic algorithm (GA)
 One of the artificial intelligent techniques
 Find an optimal radio solution in response to the
dynamic spectrum usage environment
 Solve the multi-objective optimization problem
 Define each of the major components
 Encoding method
 Recombination operators
 Fitness evaluation and selection
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Algorithms for the reasoning
engine
 Encoding the radio solution
 Fitness function
 Elitism
 Selection process and GA operations
 Termination of algorithm
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GA-based reasoning engine
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Encoding the radio solution
 GA works with the chromosomes, which are the
representations of solutions
 Map the radio adaption into a chromosome
 An initial population or a set of chromosomes is
randomly generated from the operational context
 Use binary encoding technique
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OR chromosome structure
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Fitness function
 Calculate the fitness for each chromosome
 The fittest chromosome will have the highest fitness
value
 Optimization problem
 Single-objective optimization
 Multi-objective optimization
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Fitness assignment mechanisms
 Weighted-sum
 Vector evaluation
 Pareto-ranking
 Rank-based
 Compromise
 Goal programming
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Objective functions and the
formulas
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Fitness formula
𝑘
𝑖=1 𝜔𝑖
±𝑓𝑖 ′ (𝑥)
𝐹=
∙𝑒
′
 𝑓𝑖 (𝑥) is the normalized objective function𝑓𝑖 (𝑥)
 𝜔 = (𝜔1 , 𝜔2 , … , 𝜔𝑘 ) is non-negative weight vector
 𝜔𝑖 = 1
 The relationship between 𝑥 and the resulting fitness is used to
identify plus or minus sign of 𝑓𝑖 ′ (𝑥)
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Elitism
 Retains the best chromosome(s) at each generation and
carries over to the next population
 Guarantee that the chromosome(s) with the best fitness
value(s) will not be lost during the selection process
 In this work, each new generation carries two
chromosomes from the previous generation to the next
one
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Selection process
 Produce the offspring from the selected parents
 Existing methods for the parental selection
 Roulette wheel sampling
 Boltzmann selection
 Rank selection
 Tournament selection
 Steady-state selection
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GA operations
 Crossover
 Two new chromosomes are created by swapping section(s) of
genes from parents according to the position(s) determined by
the crossover points
 Mutation
 The random modification is performed to the randomly
selected gene(s) to introduce new candidate(s) to the
population
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GA operations (cont.)
 The important parameters
 Crossover rate
 Mutation rate
 In this work
 Crossover rate is 0.6
 Mutation rate is 0.001
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Termination of algorithm
 The search continues until the termination criterion is
met
 Existing criteria
 Maximum number of generations
 Stability of the fitness of best individual
 Convergence of population
 Online and off-line performances
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Termination of algorithm (cont.)
 Convergence of population
 A gene is said to have converged when 95% of the population
share the same value. The population is said to have converged
when all of the genes have converged
 The value of genes is expressed as follows:
1
𝑛
𝐶 𝑖 = max[
𝑛
𝑘=1 𝑏(𝑘, 𝑖, 1) ,
𝑛
𝑘=1 𝑏(𝑘, 𝑖, 0)]
1, if the 𝑖th gene of the
𝑏 𝑘, 𝑖, 𝑣 =
𝑘th individual is 𝑣
0, else
𝐶1 =
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1
𝐿
𝐿
𝑖=1 𝐶(𝑖)
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System evaluation via MatLab
simulations
 Grefenstette studied the variation of six GA parameters
 Population size
 Crossover rate
 Mutation rate
 Generation gap
 Scaling window
 Selection strategy
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System evaluation via MatLab
simulations (cont.)
 Settings for GA parameters
 Population size = 50
 Crossover rate = 0.6
 Mutation rate = 0.001
 Selection strategy = Elitist strategy
 Number of generation = 1000
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Convergence and fitness plots

Average convergence and
fitness
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vs.
Example plot for fitness and
convergence from a single test
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Outline
 Introduction
 Decision making framework for the OR
 GA approach for the decision making engine
 Development of demonstration platform for the decision
making engine
 Test cases and engine performance observation
 Conclusion
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Implementation platform for the OR
 Hardware
 USRP motherboard
 Four ADC and four DAC
 Fit with different daughterboards to cover different frequency ranges
 RFX2400 transceiver daughterboard
 Cover the frequency range from 2.3 to 2.9 GHz
 Support the maximum transmit power of 50mW
 Quad Patch 2.4GHz antenna
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Implementation platform for the OR
(cont.)
 Software
 GNU Radio
 An open-source software
 Interface between the OR decision making engine and the RF frontend
 Use a combination of Python and C++ programming models
 Provides a library of signal processing blocks
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Implementation platform for the OR
(cont.)
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Outline
 Introduction
 Decision making framework for the OR
 GA approach for the decision making engine
 Development of demonstration platform for the decision
making engine
 Test cases and engine performance observation
 Conclusion
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RF scanner in the 2.4 GHz ISM
band
 The sensing information is recorded and can be
processed to detect the spectrum opportunity
In an uncontrolled
environment
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vs.
The other RF frontend
transmits data at a center
frequency of 2.442 GHz
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MatLab screen shot as decision being
made by the OR engine
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Power spectrum observed by spectrum
analyzer
 The spectrum analyzer is used to capture the power
spectrum of the entire 2.4 GHz band
Other device starts data
transmission using the channel vs.
currently used by OR terminal
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OR terminal switches to
reallocated channel
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Outline
 Introduction
 Decision making framework for the OR
 GA approach for the decision making engine
 Development of demonstration platform for the decision
making engine
 Test cases and engine performance observation
 Conclusion
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Conclusion
 Present the system architecture and algorithms of the
proposed OR decision making framework
 The GA-based reasoning engine is developed under
MatLab environment, where the system stability is
observed through the simulation
 The benefit of the proposed GA-based approach is in its
capacity to cope with multiple objectives simultaneously
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Thanks for your listening