Abstract—Radio Frequency (RF) front-end system level optimization is a multi-dimensional optimization problem and is usually an arduous and experience-based work because all system parameters trade with each other. Since memetic algorithm (MA) and particle swarm optimization (PSO) present efficient ways for multi-dimensional optimization problems, the two methods were used to optimize the key system parameters of a 5.8 GHz RF front-end. The transfer function for the output signal-to-noise ratio (SNRout) was firstly derived based on the gain, noise figure and inter-modulation product of each sub-block component, and it was used as the required fitness function for the optimization process. Both of the MA and the PSO methods provide a fast convergence, and the design parameters were obtained for the optimum SNRout values in both cases. The effectiveness of the two methods was compared. In order to verify the optimized parameters, ADS simulations was used and the final results show that the proposed methods work efficiently.
Index Terms—RF front-end, fitness function, Memetic Algorithm, Particle Swarm Optimization, ADS simulation
L. Jiang, Y. Y and H. Yu in are with college of Computer Science and Software Engineering, Shenzhen University and Shenzhen City Key Laboratory of Embedded System Design, Shenzhen, Guangdong, China, P. R.
Y. Li and Z. Ji are with college of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong, China, P.R.
Cite: Lai Jiang, Yan Yin, Hang Yu, Yan Li, and Zhen Ji, "System Level Optimisation of a 5.8 GHz ETC Receiver using Memetic Algorithm and PSO Method," International Journal of Modeling and Optimization vol. 1, no. 3, pp. 263-268, 2011.
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