Computer-Aided Optimization of a 5.8 GHz ETC Receiver Front-End Design Using Memetic Algorithm

Abstract:

Systematic level design and optimization of a Radio Frequency (RF) receiver is a multi-dimensional problem and it is usually an arduous and experience-based work because of the trade-off between various system parameters. In the mean while, co-operated with Genetic Algorithm (GA) and local search, Memetic Algorithm (MA) gradually becomes popular to solve multi-dimensional optimization problems, and it can be an efficient method for computer-aided design of RF receivers. In this paper, the MA method was adopted to aid the design of a 5.8 GHz RF receiver front-end. The transfer function for the signal-to-noise ratio at the front-end output (SNRout) was first derived based on the gain, noise figure and inter-modulation product of each individual circuit component, and the transfer function was then used as the required fitness function for the MA method. The efficacy of the proposed method was validated by ADS simulations.

• Periodical: Applied Mechanics and Materials (Volumes 157-158)
• Pages: 1601-1607
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.157-158.1601
• Online since: February 2012
• Authors: Lai Jiang, Hang Yu, Yan Li, Yan Yin, Zhan Li
• Keywords: ADS Simulation, Computer Aided Design (CAD), Fitness Function, Memetic Alogrithm, RF Front-End

References

[1] T. Monahan, 'War Rooms' of the Street: Surveillance Practices in Transportation Control Centers, The Communication Review 10, p.367–389. (2007).

DOI: 10.1080/10714420701715456

[2] A. Gühnemann, R. P. Schäfer, K. U. Thiessenhusen and P. Wagner, New Approaches to Traffic Monitoring and Management by Floating Car Data, The 10th World Conference on Transport Research, (2004).

[3] P. J. Tarnoff, D. M. Bullock, E. Stanley et al., Continuing Evolution of Travel Time Data Information Collection and Processing, Transportation Research Board Annual Meeting, (2009).

[4] GB/T 20851-2007, Electronic toll collection-Dedicated short range communication Interface with Roadside Unit and Lane Controller, (2007).

[5] J. H. Holland, Adaptation in natural articial systems. 2nd edition, MIT Press, (1992).

[6] M. H. Lim, Y. Yu and S. Omatu, Ecient Genetic Algorithms using Simple Genes Exchange Local Search Policy for the Quadratic Assignment Problem, Computa-tional Optimization and Applications, 15(3)249-268, March, (2000).

[7] Y. S. Ong and A. J. Keane, Meta-Lamarckian in Memetic Algorithm, IEEE Trans. Evolutionary Computation, 8(2)99-110, (2004).

DOI: 10.1109/tevc.2003.819944

[8] Y. S. Ong, M. H. Lim, N. Zhu and K. W. Wong, Classification of Adaptive Memetic Algorithms: A Comparative Study, IEEE Transactions On Systems, Man and Cy- bernetics - Part B, 36(1)141-52, (2006).

DOI: 10.1109/tsmcb.2005.856143

[9] Z. X. Zhu, Y. S. Ong, M. Dash, Markov Blanket-Embedded Genetic Algorithm for Gene Selection, Pattern Recognition, (2007).

DOI: 10.1016/j.patcog.2007.02.007

[10] C. Nguyen, Y. S. Ong, and H. M. Lim, A probabilistic memetic framework, IEEE Trans. Evol. Comput., vol. 13, no. 3, p.604–623, (2009).

DOI: 10.1109/tevc.2008.2009460

[11] P. Schwefel, Evolution and Optimum Seeking, John Wiley&Sons, (1995).