DESIGN OF A MINIATURIZED DUAL-BAND ANTENNA USING PARTICLE SWARM OPTIMIZATION

Transcription

1 Forum for Electromagnetic Research Methods and Application Technologies (FERMAT) DESIGN OF A MINIATURIZED DUAL-BAND ANTENNA USING PARTICLE SWARM OPTIMIZATION Waroth Kuhirun,Winyou Silabut and Pravit Boonek November, 2016 Electrical Engineering Department Faculty of Engineering Kasetsart University Bangkok Thailand

2 Copyright The use of this work is restricted solely for academic purposes. The author of this work owns the copyright and no reproduction in any form is permitted without written permission by the author. 1

3 Abstract There are various optimization techniques; one of which is particle swarm optimization. Particle swarm optimization is an evolutionary computation technique based on the movement and intelligence of swarms. This paper presents a design of a miniaturized dual-band patch antenna using particle swarm optimization with moment of methods. Keywords Particle Swarm Optimization; Dual-Band Antenna; Patch Antenna; 2

4 Biography Waroth Kuhirun is Associate Professor at the Electrical Engineering Department at Kasetsart University, Thailand. His research work is focused on computational electromagnetics and antenna engineering. He is also working on algorithmic trading using digital signal processing. Winyou Silabut is a D.Eng. student at Kasetsart University, Thailand. His research work is mainly on designing miniaturized antennas using particle swarm optimization. Pravit Boonek is a D.Eng. student at Kasetsart University, Thailand. His research work is mainly on designing mainbeam steerable antenna elements. 3

5 Outline Introduction Coding of a Patch Antenna Optimization setup and Fitness Function Optimization Conclusions Acknowledgement References 4

6 Introduction An antenna is an important part in Wireless Network Systems. Therefore, designing a suitable antenna is of importance. By the fact that designing an antenna empirically is expensive, it can be superseded by designing an antenna using a computer. In this approach, the design of a miniaturized antenna can be optimized using an optimization technique, for example, particle swarm optimization. Introduced in [1], particle swarm optimization is an evolutionary computation technique based on the movement and intelligence of swarms. There are various variations of particle swarm optimization. Further discussion can be found in [2]-[6]. The optimization is initialized with a random swarm of particles (a population of potential solutions) P1, P2,..., P n. Each particle flies through the search space with velocity adjusted by 3 factors: 1) Inertia factor: This factor prevents each individual particle from drastically changing in direction. 2) Cognitive factor: The effect of this factor is that each individual particle is drawn back to its local best position (Pbest) 3) Social factor: The effect of this factor is that each individual particle (potential solution) is drawn toward the global best position (Gbest) found by the whole population of particles. 5

7 The local best position (Pbest) and the global best position (Gbest) refer to the best position fittest to the so-called fitness function found by each individual and by the whole swarm of particles, respectively. The fitness function corresponding radiation characteristics can be evaluated by solving for current on the patch antenna. Radiation characteristics can then be calculated directly from the current. So can the fitness t function. The velocity of particle i at generation t, denoted by,can be expressed as V = ωv + cα ( P P ) + c α ( G P ) (1) t t 1 t 1 t 1 t 1 t 1 i i 1 1 i, best i 2 2 best i Referring to Eq. 1, the velocity consists of three components: the inertia component t 1 ωv t 1 t 1 i, the cognitive component c1α 1 ( Pi, best Pi ) and the social component ( t 1 t 1 c2α 2 G ) best Pi.These components are associated with the inertia factor, the cognitive factor and the social factor, respectively. It should be noted that ωα, and α 1 2are random numbers whereas c and c 1 2 are learning factors. Usually, c1 = c2 = 2. The position of particle i t at generation t, denoted by P i, can be expressed in terms of the position and velocity of particle at the previous generation as shown in Eq. 2 i V i P = P + V t t 1 t i i i (2) 6

8 Particle swarm optimization searches for the design of antenna best-suited to the specified characteristic(s), for example, low return loss. The specified characteristic(s) can be calculated once the current distribution on the antenna structure is obtained. The current distribution on antenna structure can be calculated using method of moments (MOM)[7]. Using particle swarm optimization with method of moments, this paper presents designing of a miniaturized patch antennas applicable for IEEE a/b/g standard. TABLE 1: Table to compare the return loss at 2.4 and 5 GHz of the initial antenna shown in Fig. 2 and the antenna optimized by particle swarm optimization. No. Parameter Initial Antenna Optimized Antenna 1 Frequency 2.4 GHz 5 GHz 2.4 GHz 5 GHz 2 Return Loss dB db dB dB 7

9 Coding of a Patch Antenna Fig. 1 shows the structure of a patch antenna. The size of the antenna is 33 cm. The patch is discretized into nxn cells of equal dimension where n = 15; each patch cell is represented by a binary number 1 or 0. 1 represents the presence of associated patch cell whereas 0 represents the absence of associated patch cell. Hence, the patch is represented by N1 = n = 225 bits. The height can be,,...,3 cm; each of which is represented by N bits varying from to, respectively. 2 = N bits Finally, feed position is place on one of individual patch cells. The feed position is represented by N bits where N can be determined by rounding up. Therefore, 3 3 2log2 n each of patch antenna (or particle) can be represented by N bits where N = N + N + N 2 N bits

10 Figure 1: The structure of a patch antenna 9

11 Optimization Setup and Fitness Function The optimization is initialized with a population of patch antennas (or particles); The population of patch antennas (or particles) consists of 30 patch antennas; each of which is coded by a random binary number. Fig. 2 shows an example of initial patch antennas which is coded with a binary number with all bits are 1. The specification for designing a patch antenna must correspond to the associated fitness function. The fitness function is evaluated to measure the fitness to the specification of each individual antenna (or each individual particle). For designing a dual band antenna operating at 2.4 and 5 GHz, in this paper, the fitness function f is as follows: where Dweight f = ( Var2.4GHz + Var5 GHz ) (3) S + S 11,2.4GHz 11,5GHz { 11,2.4 0,max( S GHz, S11,5GHz ) 15dB D weight = 1000, otherwise 10

12 Figure 2: An initial patch antennas coded with a binary number all bits of which are 1 11

13 Also, it should be noted that Var2.4GHz and Var5GHz in 3 refer to the variance of the radiation pattern measured at the frequency of 2.4GHz and 5GHz, respectively. The variance Var is approximated by Var = θ N θ φ N φ ( x x) N 2 (4) where N θ = N φ = { 0,10, 20,...,90 } { 0,10,...,350 } and N =

14 Optimization Fig. 3 shows the miniaturized patch antenna optimized using particle swarm optimization. Fig. 4 shows the plot of S 11 vs frequency. Additionally, for the optimized antenna, Table 1 shows that the return loss at 2.4 GHz and the return loss at 5 GHz equal db and db, respectively. That is, for the optimized antenna, S S 11,2.4GHz = db and 11,5GHz = db. In addition, Figs. 5 and 6 show radiation 0 0 pattern of the optimized antenna for = 30 and 90, respectively at the operating frequency of 2.4 GHz where as Figs. 7 and 8 show radiation pattern of the optimized 0 0 antenna for = 30 and 90, respectively at the operating frequency of 5 GHz. 13

15 Figure 3: A miniaturized patch antenna optimized using Particle Swarm Optimization with Method of Moments 14

16 Figure 4: Plot of return loss ( S 11 ) vs frequency 15

17 (a) Figure 5: Radiation pattern of the antenna at = 0 30 (b) (a) 2.4 GHz (b) 5 GHz (a) Figure 6: Radiation pattern of the antenna at = (b) 0 90 (a) 2.4 GHz (b) 5 GHz 16

18 Conclusions In this paper, a patch antenna is designed using particle swarm optimization with method of moments to support IEEE a/b/g standard. That is, the designed patch antenna can operate at 2.4 and 5 GHz. 17

19 Acknowledgement The authors would like to thank Kasetsart University Research and Development Institute and Petroleum and Petrochemical College, Chulalongkorn University for financial support. 18

20 References [1] J. Kennedy and R. Eberhart,Particle Swarm Optimization, Proc. IEEE Conf. Neural, Vol. 4, pp , [2] J. Robinson, Y. Rahmat-Samii,Particle Swarm Optimization in Electromagnetics, IEEE Trans. Antennas Propagat., vol. 52, No. 2, pp ,2004. [3] A. Marandi, F. Afshinmanesh, M. Shahabadi, and F. Bahrami, Boolean Particle Swarm Optimization and Its Application to the Design of a Dual-Band Dual- Polarized Planar Antenna, IEEE Congress on Evolutionary Computation, pp ,2006. [4] R. Duvigneau, A Multi-Level Particle Swarm Optimization Strategy for Aerodynamic Shape Optimization,EUROGEN,2007. [5] A. P. Engelbrecht,Fundamentals of Computational Swarm Intelligence, 1st edition, Wiley, England, pp ,2005. [6] [7] S. N. Makarov,Antenna and EM Modeling with Matlab,2nd ed, New York: J.Wiley and Sons,