Simulation Results on Wavelength Selection Problem

For the purpose of fast prototyping and to be able to draw a preliminary conclusion, the problem investigated in [4.28] was studied and results are compared against reliability and complexity of MATLAB GA Toolbox (in Chapter 5). The problem considers the selection of the most appropriate transmission wavelength of low power infrared lasers, and the international code of visibility presented in [4.25] has been adapted for channel modelling. The ASGA is able to show the channel performance at any instant, and it has the ability to monitor the percentage improvement at subsequent iterations as shown in figure 4.13 (attenuations versus generations) giving more interest at higher wavelengths (1000nm-1500nm) that are more eye-safe. Both approaches led to acceptable results, and assured that the ASGA is capable to tackle the problem with similar complexity and reliability, published in [4.28]. The algorithm is extended to include all parameters in chapter 5 and a detailed comparison of complexity and reliability appears in Table 5.5.

87 | P a g e

Figure 4.13 ASGA for wavelength selection of low power laser systems operating between 700nm to 1600nm in different weather conditions (Haze, Haze-Fog, and Fog).

4.5 Conclusion

This chapter has shown a new way of selecting the transmission wavelength for various weather conditions. The results achieved appear to be very realistic and it increased the confidence such that the work could be developed so that the selection algorithm can then use many other parameters. In chapter 5, the proposed application-specific genetic algorithm (ASGA) is utilized, for selecting the overall parameters of the optical wireless channel. The algorithm is built to select the optimal parameters under certain weather conditions and link characteristics that would result in a minimum fade margin and hence; larger space for control.

88 | P a g e

References:

[4.1] C. W. Ahn, R. Ramakrishna, “A genetic algorithm for shortest path routing problem and the sizing of populations”,IEEE Transactions on Evolutionary Computation, vol. 6, no. 6, pp. 566 – 579, Dec. 2002.

[4.2] G. Yan, “Design of qosanycast network cluster balance based on genetic algorithm”,in Proc. International Conference on Signal Processing Systems, pp. 610 –614, May 2009.

[4.3] J. Arabas, S. Kozdrowski, “Applying an evolutionary algorithm to telecommunication network design”,IEEE Transactions on Evolutionary Computation, vol. 5, no. 4, pp. 309 –322, Aug. 2001.

[4.4] S. Ullah, F. Karray, J. -M. Won, “Non-dominated sorting evolution strategy- based k-means clustering algorithm for accent classification”,in Proc. International Conference on Pattern Recognition, pp. 1 –4, Dec. 2008.

[4.5] S. Yang, H. Cheng, F. Wang, “Genetic algorithms with immigrants and memory schemes for dynamic shortest path routing problems in mobile ad hoc networks”, IEEE Transactions on Systems, Man, and Cybernetics, vol. 40, no. 1, pp. 52 –63, Jan. 2010.

[4.6] H. Sayoud, K. Takahashi, B. Vaillant, "Designing communication network topologies using steady-state genetic algorithms", IEEE Communications Letters, vol.5, no.3, pp.113-115, Mar. 2001.

[4.7] C.-S.Lee, S.-M.Guo, C.-Y. Hsu, "Genetic-based fuzzy image filter and its application to image processing", IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol.35, no.4, pp.694-711, Aug. 2005.

89 | P a g e

[4.8] M.M. Rizki, M.A. Zmuda, L.A. Tamburino, "Evolving pattern recognition systems", IEEE Transactions on Evolutionary Computation, vol. 6, no. 6, pp. 594- 609, Dec. 2002.

[4.9] C. Ming, Y. Zhengwei, "Classification Techniques of Neural Networks Using Improved Genetic Algorithms", in Proc. Second International Conference on Genetic and Evolutionary Computing (WGEC08), pp.115-119, 2008.

[4.10] D. E. Goldberg, “Genetic Algorithms in Search, Optimization and Maching Learning”, Addison-Wesley publishing company, INC, 1989.

[4.11] J. Zhang, H. S.-h.Chung, W.-L. Lo, “Clustering-Based Adaptive Crossover and

Mutation Probabilities for Genetic Algorithms”, IEEE Transactions on Evolutionary Computation, vol. 11, no.3, pp. 326-335, June 2007.

[4.12] K. Man, K. Tang, S. Kwong, “Genetic Algorithms :Concepts and Designs”, Springer, London, 1999.

[4.13] K. De Jong, W. Spears, “A formal analysis of the role of multi-point crossover in genetic algorithms”, Annals of Mathematics and Artificial Intelligence, vol. 5, no. 1, pp. 1–26, 1992.

[4.14] Y. Rahmat-Samii, E. Michielssen, “Electromagnetic optimization by genetic algorithms”, John Wiley&Sons, New York, USA, 1999.

[4.15] M. Mitchell, “Review of L. D. Davis Handbook of Genetic Algorithms”, Van NostrandReinhold, New York, 1991, Available: <http://web.cecs.pdx.edu/~mm/davis-review.pdf>, Accessed: [21st June 2012].

[4.16] T. Mitchell, “Machine Learning”, McGraw Hill, 1997.

90 | P a g e

York, 1962.

[4.18] Mc. B. Arthur, E. Korevaar, "Comparison of laser beam propagation at 785nm and 1550 nm in fog and haze for optical wireless communications", in Proc. Of SPIE, Vol. 4214, 2001, 26-37.

[4.19] P.L. Eardley, D.R. Wisely, “1 Gbit/s Optical Free Space Link Operating over 40 m Systems and Applications”, IEEE Proceedings in Optoelectronics, 143(6), 330–333, 1996.

[4.20] M.W. Madea, et al. “The effect of four-wave mixing in fibres on optical frequency-division multiplexed systems, Journal of Lightwave Technology, vol. 8. pp. 1402-1408, Sept. 1990.

[4.21] I. I. Kim, B.McArthur, E. Korevaar , "Comparison of laser beam propagation at 785 nm and 1550 nm in fog and haze for optical wireless communications", in SPIE Proceedings Vol. 4214, Optical Wireless Communications III, pp.26-37, CA 92121, 2001.

[4.22] R.G. Smith, S.D. Personick, “Receiver design of optical fiber communications systems”, in Semiconductor Device for Optical Communication, H. Kressel, Ed., New York: Springer-Verlag, 1980.

[4.23] D. Giggenbach, and H. Henniger, “Fading-loss assessment in atmospheric freespace optical communication links with on-off keying,”,Optical Engineering, vol. 47, April 2008, pp. 046001-1 - 046001-6.

[4.24] S. S. Muhammad, B. Flecker, E. Leitgeb, M.Gebhart, “Characterization of fog attenuation in terrestrial free space optical links,” Optical Engineering, vol. 46(6), June 2007, pp. 066001.

91 | P a g e

[4.25] R. R. Iniguez, S. M. Idrus, and Z. Sun, "Atmospheric transmission limitations," in Optical Wireless Communications - IR for Wireless Connectivity London: Taylor & Francis Group, LLC, 2008, p. 40.

[4.26]M.A.Bramson, “Infrared Radiation, A Handbook for Applications”, Plenum Press, 1969, p.602.

[4.27] A. Rehman, S. Muhammad, “Statistical Analysis of Received Signal Strength in Fog Using Sliding Window Technique for FSO Links”, 8th International Conference on Frontiers of Information Technology, Islamabad, Pakistan, December 21-23, 2010.

[4.28] A. El. Yakzan, R.J. Green and E. L. Hines, “A Genetic Algorithm based Selection of a Transmission Wavelength in the LOS Optical Wireless Channel”, International Conference on Transparent Optical Networks, University of Warwick, UK, July, 2012.

[4.29] F. G. Lobo, D. E. Goldberg, and M. Pelikan, “Time complexity of genetic algorithms on exponentially scaled problems”, in Darrell Whitley et al., editors,Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000), pages 151-158, Las Vegas, Nevada, 2000. Morgan Kaufmann.

[4.30] B. Rylander, J. Foster, “Computational Complexity and Genetic Algorithms”, Proceedings of the World Science and Engineering Society's Conference on Soft Computing, USA, 2001.

92 | P a g e

CHAPTER V: An Application-Specific Genetic Algorithm for Link