Optimization of Mobile Phone Antennas using Generic Algorithms and Network Parallelization X. L. Chen*1 , E. Ofli2 , N. Chavannes2 , and N. Kuster1 1 IT’IS
2 Schmid
Foundation, Zurich, Switzerland and Partner Engineering AG, Zurich, Switzerland E-mail:
[email protected]
Introduction In this study, Genetic Algorithms (GA) [1] in conjunction with network parallelization technique are utilized for the design and optimization of mobile phone antennas in a CAD derived environment. On the basis of a commercial mobile phone CAD model, three types of internally embedded multi-band antenna are designed and optimized within a fixed volume. Through the use of network parallelization, optimization time can be significantly reduced.
Genetic Algorithms and Network Distributed Optimization Optimization algorithms have been widely applied to antenna optimizations in the past decades. With the advancement of computer hardware such as multi-core CPUs and GPUs [3], coupled with efficient computational electrodynamics modeling techniques e.g. FDTD [2], complex antenna optimization problems can now be solved proficiently. Genetic Algorithms [1] belong to a particular class of evolutionary algorithms that use techniques inspired by evolutionary biology, such as inheritance, mutation, selection, and crossover. GA work very effectively on combinatorial problems and is less susceptible to pitfalls such as “stuck at local optima” than the traditional algorithms, e.g. gradient search methods. GA are especially efficient for optimization problems that do not have a known good starting point and seek to achieve global optimum within the range of variable parameters. Based on the evolution-like process, optimization parallelization is realized in a FDTD solver [4] by utilizing network clustered computer systems. The optimization time is reduced proportionally by the number of computer systems utilized.
Antenna Designs and Optimization Setup In this investigation, a candy bar type mobile phone with a slim form factor is employed as shown in Figure 1. The entire CAD phone consists of over two hundred parts, providing a “near final hardware” simulation model. The industrial antenna design for the selected phone model is a folded monopole antenna (FMA) as shown in Figure 2. Alternative antenna designs are realized in the volume occupied by this antenna, which is roughly 45mm × 14.5mm × 8mm. The antennas are designed to operate in frequency bands of GSM850, GSM900, DCS1800 and PCS1900. Besides the folded monopole antenna design, the antenna types investigated in this study are the Planar Inverted F Antenna (PIFA) [5] and the Folded Inverted Conformal Antenna (FICA) [6] as shown in Figure 2. The antenna elements subject to optimization are modeled as parameterized objects in [4]. The optimizations are
performed in a free space environment. The parameterized antennas are simulated targeting a return loss of −5dB at band edges for a quad-band operation. Once optimized, the antenna geometries are subjected to head and hand phantoms loading environment to evaluate their performance in real usage conditions. A homogeneous Specific Anthropomorphic Mannequin (SAM) head phantom and anatomical hand model are used in the simulations. The dielectric properties of the head and hand phantoms are based on the human tissue measurements data described in [7] and [8], respectively.
Figure 1: Mobile phone model used in this study, photo of actual phone (left) and CAD model (right).
Figure 2: (clockwise) Folded Monopole Antenna (FMA), Planar Inverted F Antenna (PIFA) and Folded Inverted Conformal Antenna (FICA).
Results and Discussions Radiation Performance in Free Space The optimizations were performed for the antenna designs in free space. All three designs achieve the desired bandwidth coverage for a quad-band operation following the predefined optimization goals. The return loss plots and TRP values of the antennas are shown in Figure 3. The averaged TRP values for each frequency band are plotted based on 33dBm and 30dBm conducted power for GSM/EGSM and
DCS/PCS bands, respectively. From the bandwidth and radiation performance aspects, all three designs demonstrate competency as an internally embedded antenna solution for the selected phone model.
Figure 3: Return loss (left) and TRP values (right) of antennas in free space after optimizations.
Radiation Performance with Head and Hand Phantoms The antenna geometries optimized in free space are simulated with head and hand phantoms to evaluate the amount of detuning. Only the head and hand phantom right side placement is investigated in this study. The band-averaged TRP plots for head phantom along and head with hand phantoms are presented in Figure 4.
Figure 4: TRP values of antennas under head phantom along (left) and head with hand phantoms (right) loading conditions.
Optimization Time and Computational Requirements The optimizations are performed on two workstations each equipped with a NVIDIA Quadro FX5600 GPU acceleration card [2]. The resulting grid for the mobile phone alone contains about 4 million FDTD cells, while the grid for the phone with head and hand phantoms contains around 12 million cells. Each broadband simulation
takes approximately 8 minutes (phone only) and 20 minutes (phone, head and hand phantoms). The numbers of simulations required for a 100% fitness in the free space condition is approximately 150. This is equivalent to a total of 10 hours of simulation time. The optimization module is configured to continue through the entire generations even after achieving the first 100% fitness; which ensures the coverage of all available optimum solutions.
Conclusion This paper presents the simulation and optimization of mobile phone antennas in a detailed CAD database environment. The demonstrated virtual prototyping of a commercial mobile phone with different antenna structures provides valuable information to assist engineers in the design and analysis of antennas in real world usage conditions. With the combinations of a well devised strategy and advanced computation power, antenna optimization can be accomplished in an effective and efficient way.
References [1] J. H. Holland, Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence, the MIT Press, April 1992. [2] D. M. Sullivan, Electromagnetic Simulation Using the FDTD Method, IEEE Press Series on RF and Microwave Technology, IEEE Press, New York, 2000. [3] GPU Hardware Acceleration: Acceleware. www.acceleware.com. [4] SEMCAD X: Electromagnetic Simulation Platform. www.semcad.com. [5] T. Taga, “Analysis of planar inverted-F antennas and antenna design for portable radio equipment”, in Analysis, Design and Measurement of Small and Low Profile Antennas, K. Hirasawa and M. Haneishi, Eds. Boston; ch. 5, Artech House, 1992. [6] C. Di Nallo and A. Faraone, “Multiband Internal Antenna for Mobile Phones”, IEEE Electronics Letters Vol.41, No.9, 28th April 2005. [7] IEEE Std 1528-2003. Recommended Practice for Determining the Peak SpatialAverage Specific Absorption Rate (SAR) in the Human Head from Wireless Communications Devices - Measurement Techniques. Institute of Electrical and Electronics Engineers, New York. 19 December 2003. [8] C. Gabriel, “Tissue Equivalent Material for Hand Phantoms”, Phys. Med. Biol. 52, pp. 4205-4210, 2007.
