1. Authors: Soamsiri Chantaraskul, Klaus Moessner Source: IET Commun., Vol.4, No.5, 2010, pp.495 - 506 Presenter: Ya-Ping Hu Date: 2011/12/23 Implementation of a genetic algorithm-based decision making framework for opportunistic radio

2. 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 2011/12/23 2

3. 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 2011/12/23 3

4. Introduction 2011/12/23 4 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

5. Introduction (cont.) 2011/12/23 5 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

6. 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 2011/12/23 6

7. Decision making framework for OR 2011/12/23 7 Key components and their relationships

8. Decision making engine 2011/12/23 8 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

9. Filtering mechanism 2011/12/23 9 Concerned value (CV) algorithm Collaborative filtering or social filtering Case-based reasoning (CBR)

10. Reasoning engine 2011/12/23 10 The operational configuration is taken into account and the preferred solution is obtained The approach is based on the GA

11. 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 2011/12/23 11

12. Genetic algorithm (GA) 2011/12/23 12 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

13. Algorithms for the reasoning engine 2011/12/23 13 Encoding the radio solution Fitness function Elitism Selection process and GA operations Termination of algorithm

14. GA-based reasoning engine 2011/12/23 14

15. Encoding the radio solution 2011/12/23 15 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

16. OR chromosome structure 2011/12/23 16

17. Fitness function 2011/12/23 17 Calculate the fitness for each chromosome The fittest chromosome will have the highest fitness value Optimization problem Single-objective optimization Multi-objective optimization

18. Fitness assignment mechanisms 2011/12/23 18 Weighted-sum Vector evaluation Pareto-ranking Rank-based Compromise Goal programming

19. Objective functions and the formulas 2011/12/23 19

20. Fitness formula 2011/12/23 20

21. Elitism 2011/12/23 21 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

22. Selection process 2011/12/23 22 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

23. GA operations 2011/12/23 23 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

24. GA operations (cont.) 2011/12/23 24 The important parameters Crossover rate Mutation rate In this work Crossover rate is 0.6 Mutation rate is 0.001

25. Termination of algorithm 2011/12/23 25 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

26. Termination of algorithm (cont.) 2011/12/23 26

27. System evaluation via MatLab simulations 2011/12/23 27 Grefenstette studied the variation of six GA parameters Population size Crossover rate Mutation rate Generation gap Scaling window Selection strategy

28. System evaluation via MatLab simulations (cont.) 2011/12/23 28 Settings for GA parameters Population size = 50 Crossover rate = 0.6 Mutation rate = 0.001 Selection strategy = Elitist strategy Number of generation = 1000

29. Convergence and fitness plots 2011/12/23 29 Average convergence and fitness vs. Example plot for fitness and convergence from a single test

30. 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 2011/12/23 30

31. Implementation platform for the OR 2011/12/23 31 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

32. Implementation platform for the OR (cont.) 2011/12/23 32 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

33. Implementation platform for the OR (cont.) 2011/12/23 33

34. 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 2011/12/23 34

35. RF scanner in the 2.4 GHz ISM band 2011/12/23 35 The sensing information is recorded and can be processed to detect the spectrum opportunity In an uncontrolled environment vs. The other RF frontend transmits data at a center frequency of 2.442 GHz

36. MatLab screen shot as decision being made by the OR engine 2011/12/23 36

37. Power spectrum observed by spectrum analyzer 2011/12/23 37 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 currently used by OR terminal vs. OR terminal switches to reallocated channel

38. 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 2011/12/23 38

39. Conclusion 2011/12/23 39 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

40. Thanks for your listening