ARTIFICIAL INTELLIGENCE APPLICATIONS IN WIRELESS NETWORKSBekir Sait Çiftler

http://bsciftler.etu.edu.tr

Artificial Intelligence

� Intelligent Agents

� Deduction

� Reasoning

� Problem solving

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� Problem solving

� Learning

Image Source:http://upload.wikimedia.org/wikipedia/commons/1/17/ArtificialFictionBrain.png

AI in Wireless Technology

� Cognitive Radios

� Dynamic Spectrum Access

� Self-Organizing Networks

� Spectrum Markets

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� Spectrum Markets

� Docitive Networks

� Swarm Intelligence

Image source:

http://blog.gonzalo-vazquez-vilar.eu/img/blog_nokia-cr.jpg

Cognitive Radio4

Artificial Intelligence for Cognitive Radios

� What is a Cognitive Radio? => Late 1990s

� Dynamic Spectrum Access (DSA)

� Spectrum Markets

� Self-organizing Networks (SONs)

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� Self-organizing Networks (SONs)

� Software Defined Radio

� Capabilities of CR;

� Observation

� Reconfiguration CE: Cognitive Engine

� Cognition

Artificial Intelligence for Cognitive Radios

� AI techniques used in CR (Cognitive Engines) [He, 2010];

� Artificial Neural Networks (ANNs)

� Metaheuristic Algorithms

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� Metaheuristic Algorithms

� Hidden Markov Models (HMMs)

� Rule-based Systems (RBSs)

� Ontology-based Systems (OBSs)

� Case-based Systems (CBSs)

Artificial Intelligence for Cognitive Radios

� Performance Factors [He, 2010];

� Responsiveness

� Complexity

� Security

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� Security

� Robustness

� Stability

AI Techniques for Wireless Networks

� Awareness

� Extracting the information

� Reasoning

� Finding an appropriate action

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� Finding an appropriate action

� Learning

� Accumulating knowledge

Artificial Neural Networks

� The study of the human brain

� A set of nonlinear functions with adjustableparameters to give a desired output

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ANN dependency graphImage Source: Wikipedia

Types of ANNs used in CR

� Multi-Layer Linear Perceptron Networks

� Back Propagation & Genetic Algorithms

� Nonlinear Perceptron Networks

� Flexible but slow!

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� Flexible but slow!

� Radial Basis Function Networks

� Local Minimum: Not a problem any more

Application of ANN to CR

� Spectrum Sensing: ANN-based signal classifier[Fehske, 2005]� Cyclostationary analysis & ANN

� Efficiency & Reliability of Signal Classification

Radio Parameter Adaptation [Reed, 2005]

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� Radio Parameter Adaptation [Reed, 2005]� CR testbed using Tektronix test equipment

� BER, maximizing the throughput, minimizing the transmissionpower

� Large-Scale CR cloud optimization[Hasegawa, 2007]

Metaheuristic Algorithms

� Explicit relations usually N/A

� Genetic Algorithms

� Fitness function

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� Fitness function

� Reproduction, mutation; Increasing fitness!

� Simulated Annealing

� Tabu Search

� Ant Colony Optimization

Application of MAs to CR

� Applying the GA to adapt the radio parameters of an SDR [Rondeau, 2004]

� The fitness function

� Link condition

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� User-application requirements

Hidden Markov Models

� Making transitions from state to state

� The states are hidden! Only outputs are observable!

� Recognition

� Probability of observation sequence

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� Probability of observation sequence

� Decoding

� Best explanation to observation sequence (ViterbiAlgorithm)

� Training, Learning

Application of HMMs to CR

� Modeling the wireless channel online for CR [Rondeau, 2004]

� The HMM is trained using the GA with data from a broadband channel sounder in a LOS additive whiteGaussian noise channel

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Gaussian noise channel

� Spectrum Sensing & DSA [Kim, Akbar, 2007] [Ghosh, 2009]

Rule-based Systems (RBS)

� Rules extracted from a specific application area

� Rule base, Inference Engine (Input => Rules => Output)

� IF conditions THEN actions [He, 2010]

� New data may or may not trigger a rule

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� New data may or may not trigger a rule

� Rule extraction with another CE

Application of RBS to CR

� For IEEE 802.22 Wireless Rural Area Network developed and evaluated [Reed, 2006];

� Lower complexity w.r.t. GA cognitive engines

� Rule database extraction [Weingart, 2007]

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� Rule database extraction [Weingart, 2007]

� Statistical analysis

Ontology-based Systems (OBS)

� Semantic => Understandable

� Logically deduction

� Ontology includes;

� Classes

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� Classes

� Instances

� Attributes

� Relations

� XML, RDF, OWL => Semantic Web

Application of OBS to CR

� Policies

� DARPA’s next-generation (XG) policy languageframework

� Future extensions of spectrum rules

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� OBR used for self-awareness & interoperabilityamong SDR nodes [Kokar, 2009]

Case-based Systems (CBS)

� Previous similar cases for problem-solving process toobtain a solution [Kolodner, 1996]

� Modules in a CBS;

� Case representation and indexing

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� Case representation and indexing

� Case selection and retrieval

� Case evaluation and adaptation

� Case database population and maintanance

� Similar to human reasoning

Application of CBS to CR

� A CE using CBR & fuzzy logic to determine thechannel type for WiMAX systems[Khedr & Shatila, 2009]

� Acceptable solution

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� Acceptable solution

� Needs to learn new cases

Coexistance of Multiple CEs22

� Cognitive Femtocells

� Insufficient to design an AI in isolation!

� Games;

� Supermodular games� Supermodular games

� Potential games

� Auction theory

� Stochastic games

Docitive cycle which extends the cognitive cycle through the teaching element[Blasco, 2011]

Docitive Networks23

Docitive Networks24

� A learning agent e.g. can take advantage of the exchange of information and expert knowledge from other agents, the so-called docitive agents. [Blasco, 2011]

Image Source: Guardian.co.uk

Swarm Intelligence in WSN25

Swarm Intelligence in WSN26

� Wireless sensor networks (WSNs) contain hundreds or thousands of those sensors nodes => Swarm!

� Path discovery for efficiency! [Barbancho, 2007]

Image Source:

Popupcity.net

Conclusion

� AI techniques lie at the heart of Cognitive Radio

� Tradeoffs

� Performance

� Costs

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� Costs

� Interdisciplanry nature

� Computer Science

� Optimization

� Mathematics

References

1) He, A.; Kyung Kyoon Bae; Newman, T.R.; Gaeddert, J.; Kyouwoong Kim; Menon, R.; Morales-Tirado, L.; Neel, J.J.; Youping Zhao; Reed, J.H.; Tranter, W.H.; , "A Survey of Artificial Intelligence for Cognitive Radios," Vehicular Technology, IEEE Transactions on , vol.59, no.4, pp.1578-1592, May 2010

2) A. Fehske, J. Gaeddert, and J. H. Reed, “A new approach to signal classification using spectral correlation and neural networks,” in Proc. 1st IEEE Int. Symp. New Frontiers DySPAN, Baltimore, MD, Nov. 8–11, 2005, pp. 144–150.

3) J. H. Reed et al., “Development of a Cognitive Engine and Analysis of WRAN Cognitive Radio

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Algorithms—Phase I,” Wireless @ Virginia Tech, Virginia Polytech. Inst. State Univ., Blacksburg, VA, Dec. 2005.

4) M. Hasegawa, T. Ha Nguyen, G. Miyamoto, Y. Murata, and S. Kato, “Distributed optimization based on neurodynamics for cognitive wireless clouds,” in Proc. IEEE 18th Int. Symp. PIMRC, Athens, Greece, Sep. 3–7, 2007, pp. 1–5.

5) T. W. Rondeau, B. Le, C. J. Rieser, and C. W. Bostian, “Cognitive radios with genetic algorithms: Intelligent control of software defined radios,” in Proc. Forum Tech. Conf. SDR, Phoenix, AZ, Nov. 15–18, 2004, pp. C-3–C-8

References29

6) T. W. Rondeau, C. J. Rieser, T. M. Gallagher, and C. W. Bostian, “Online modeling of wireless channels with hidden Markov models and channel impulse responses for cognitive radios,” in Proc. IEEE MTT-S Int. Microw. Symp. Dig., Fort Worth, TX, Jun. 6–11, 2004, pp. 739–742.

7) K. Kim, I. A. Akbar, K. K. Bae, J.-s. Urn, C. M. Spooner, and J. H. Reed, “Cyclostationary approaches tosignal detection and classification in cognitive radio,” in Proc. 2nd IEEE Int. Symp. New FrontiersDySPAN, Dublin, Ireland, Apr. 17–20, 2007, pp. 212–215.

8) C. Ghosh, C. Cordeiro, D. P. Agrawal, and M. B. Rao, “Markov chain existence and hiddenMarkovmodels in spectrum sensing,” in Proc. IEEE Int. Conf. PerCom, Galveston, TX, Mar. 9–13, 2009, pp. 1–6.

9) J. H. Reed et al., “Development of a cognitive engine and analysis of WRAN cognitive radioalgorithms—Phase II,”Wireless@Virginia Tech, Virginia Polytech. Inst. State Univ., Blacksburg, VA, Dec. 2006.

10) T. Weingart, D. C. Sicker, and D. Grunwald, “A statistical method for reconfiguration of cognitiveradios,” IEEE Wireless Commun., vol. 14, no. 4, pp. 34–40, Aug. 2007.

11) M. M. Kokar and L. Lechowicz, “Language issues for cognitive radio,” IEEE J. Sel. Areas Commun., vol. 97, no. 4, pp. 689–707, Apr. 2009.

References30

12) J. L. Kolodner and D. Leake, “A tutorial introduction to case-based reasoning,” in Case-Based Reasoning: Experiences, Lessons and Future Directions. Cambridge, MA: MIT Press, 1996, pp. 31–65.

13) M. Khedr and H. Shatila, “Cogmax—A cognitive radio approach for WiMAX systems,” in Proc. IEEE AICCSA, Rabat, Morocco, May 10–13, 2009, pp. 550–554.

14) Blasco, P. M., “Docitive Networks − A Step Beyond Cognition −” Master Thesis, UniversitatPolit`ecnica de Catalunya, Jan 2011

15) Julio Barbancho, Carlos León, F.J. Molina, Antonio Barbancho, Using artificial intelligence in 15) Julio Barbancho, Carlos León, F.J. Molina, Antonio Barbancho, Using artificial intelligence in routing schemes for wireless networks, Computer Communications, Volume 30, Jun 2007

Thanks31

� My special thanks to;

� Prof. Dr. Halim Yanikomeroglu

� Furkan Alaca (MSc Student @ Carleton University)

THANK YOU!ANY QUESTIONS?Downloadable at:

http://bsciftler.etu.edu.tr/research.htm