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Automatic AI-Driven Design of Mutual Coupling Reducing Topologies for Frequency Reconfigurable Antenna Arrays

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Automatic AI-Driven Design of Mutual Coupling Reducing Topologies for Frequency Reconfigurable Antenna Arrays \ Zhang, J., Akinsolu, M. O., Liu, B. and Vandenbosch, G. A.E. (2020) Automatic AI-driven design of mutual coupling reducing topologies for frequency reconfigurable antenna arrays. IEEE Transactions on Antennas and Propagation, (doi: 10.1109/TAP.2020.3012792) The material cannot be used for any other purpose without further permission of the publisher and is for private use only. There may be differences between this version and the published version. You are advised to consult the publisher’s version if you wish to cite from it. http://eprints.gla.ac.uk/221985/ Deposited on 11 August 2020 Enlighten – Research publications by members of the University of Glasgow http://eprints.gla.ac.uk IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION 1 Automatic AI-Driven Design of Mutual Coupling Reducing Topologies for Frequency Reconfigurable Antenna Arrays Jiahao Zhang, Mobayode O. Akinsolu, Bo Liu, and Guy A. E. Vandenbosch processes for the parasitic elements. As a result, this process is quite Abstract—An automatic AI-driven design procedure for mutual dependent on the experience of the designers. Moreover, array coupling reduction and a novel isolator are proposed for a frequency configurations become more and more complicated in modern reconfigurable antenna array. The design process is driven and expedited communication systems, involving multiband antennas, by the parallel surrogate model-assisted differential evolution for antenna synthesis (PSADEA) method. The reconfigurable array element can reconfigurable antennas and so on. In these cases, obtaining a design switch its operation between the 2.5 GHz ISM band and the 3.4 GHz that reduces mutual coupling in a reasonable time becomes a great WiMAX band. By introducing the proposed isolator, the mutual coupling challenge. Therefore, it is an advantageous idea to propose a simple in the higher and lower band is reduced by 8 dB and 7 dB, respectively. and generic mutual coupling reduction method for diverse array The reconfigurable array was prototyped, and measurements agree well configurations serving complex applications, without requiring with simulations, verifying the validity of the proposed concept. Although used for a specific antenna in this communication, the proposed AI-driven extensive experience from the designers. design strategy is generic and can easily be employed for other array Simulation-driven antenna designs have attracted attention in recent topologies. years. In [14] and [15], the performance of MIMO antennas was improved based on simulation-driven optimizations, considering Index Terms—Mutual coupling reduction, reconfigurable antenna, mutual coupling levels. However, general mutual coupling reduction frequency reconfigurability, surrogate modeling, optimization. approaches were not explicitly investigated. In this communication, a generic mutual coupling reduction method for diverse arrays is realized for the first time by introducing the I. INTRODUCTION concept of AI-driven design optimization or automation. Considering With the development of modern wireless communication systems, the requirements on generality, ability to obtain high-quality solutions multiple antenna technologies are becoming increasingly important for complex structures, and efficiency, the surrogate model-assisted [1], due to their ability to offer high gain, high resolution, global optimization technique is chosen. In particular, the parallel beamforming, and beam scanning. When two or more antennas are surrogate model-assisted differential evolution for antenna synthesis placed next to each other on a single platform, they are mutually (PSADEA) method [16], [17] was employed. SADEA is an algorithm coupled in different ways depending on the type of antennas and their series dedicated to antenna optimization [16]-[19]. PSADEA is the arrangement. For example, when two microstrip antennas are placed in latest method in this series. It offers a 3 to 20 times optimization proximity to each other, they couple to each other through the efficiency improvement and a higher optimization ability compared to substrate/air layer (with the surface wave) and the air half-space (with standard global optimization methods for practical antenna designs the space wave). Typically, strong mutual coupling between antenna [16]-[19]. The essential features in PSADEA include the use of: (1) elements typically reduces the performance of any given multiple Gaussian process surrogate modeling, which is a kind of supervised antenna system. Particularly, it causes scan blindness in phased arrays, learning technique for the prediction of the antenna performance, (2) limits the practical packing density of antenna arrays, and degrades the complementary differential evolution search operators to explore the diversity performance of multiple-input multiple-output (MIMO) design space, (3) the reinforcement learning method to employ the systems. search operators self-adaptively, and (4) the surrogate model-aware Mutual coupling reduction has been a topic of interest since the evolutionary search framework, which ensures the effective synergy early days of antenna array design. The most commonly reported of online surrogate modeling and global optimization. approaches include the use of: (1) defected ground structures (DGS) In this communication, an automatic PSADEA-driven design of an [2], [3], (2) electromagnetic band-gap (EBG) structures [4], [5], (3) innovative mutual-coupling-reducing parasitic element is proposed. soft surfaces [6], [7], (4) parasitic elements [8], [9], and (5) The antenna topology considered is a dual-band frequency combinations of these [10]-[12]. reconfigurable antenna, which can switch its operating band between Among these methods, the method of using parasitic elements has the 2.5 GHz ISM band and the 3.4 GHz WiMAX band. With the the clear advantage of resulting in a simple topology. The principle of resulting parasitic element, the mutual coupling in both bands is using parasitic elements is to reduce mutual coupling by creating reduced to values below -20 dB. To the best knowledge of the authors, reverse coupling [13]. Antenna array configurations (antenna element it is the first time that a mutual-coupling-reducing parasitic element is topology, element spacing, etc.) tend to employ varying design automatically designed based on machine learning and evolutionary computation. This procedure shows the advantages of generality, high design quality, and high efficiency without the need for reasonably Manuscript received January 22, 2020. good initial design parameters. J. Zhang and G. A. E. Vandenbosch are with the ESAT-TELEMIC Research Division, Department of Electrical Engineering, KU Leuven, 3001 Leuven, Belgium (e-mail: [email protected]). M. O. Akinsolu is with the Faculty of Arts, Science and Technology, Wrexham Glyndwr University, II. PSADEA-DRIVEN DESIGN OF ISOLATORS Wrexham LL11 2AW, U.K. B. Liu is with James Watt School of Engineering, The mutual coupling reduction technique is illustrated in Fig. 1 for a University of Glasgow, G12 8QQ, Scotland. Color versions of one or more of the figures in this communication are two-element antenna array. Ant. 1 is excited by a current I. Through available online at http://ieeexplore.ieee.org. mutual coupling, a current αI is induced on Ant. 2, where α is the Digital Object Identifier IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION 2 Top view Top view gnd_w y I αI+β β I k 1 2 rad_w x β1I z b Shorting pin e Isolator p1_d Feed probe a rad_l p2_d Shorting pin gnd_l y Ant. 1 Ant. 2 p3_d Ant. 1 Ant. 2 x z l h Side view j i z z Side view 0.5e y y c x sub1_h x air_h 0.5b Sub1 Fig. 1. Operation mechanism of the parasitic element-based mutual coupling d Air gap 0.5e reduction method. The detailed design of this array is given in Fig. 3. Sub2 sub2_h g f Fig. 3. The topology of the proposed isolator. Sample the Select training data, 0 design space Local GP modeling -10 Reflection coefficient Prescreening, Stopping Mutual coupling Yes Select n estimated top criteria? candidates -20 Output No Parallel EM parameters (dB) - simulations, S -30 Select the k best Update database designs Single element Without isolator With isolator Update DE mutation -40 strategies selection 2 2.2 2.4 2.6 2.8 3 Self-adaptive rate Freq. (GHz) child solutions Fig. 4. S-parameters of Design 1. The dashed lines indicate reflection generation coefficients, the solid lines indicate the mutual coupling. Fig. 2. PSADEA flow diagram. As shown in Fig. 2, PSADEA is initialized using a small number of samples from the design space. In each iteration, child solutions are coupling coefficient. By adding an isolator, a new situation is generated by applying three types of DE mutation operators generated, the coupling situation of which can be worked out to the self-adaptively to a fixed number (k) of top-ranked candidate solutions. The self-adaptiveness comes from the fact that the probability to first order. Also, a current β1I is now induced in the isolator, generating employ each DE mutation operator is related to the number of cases in its turn an induced current β1β2I in Ant. 2, where β1 is the coupling that, using such a mutation strategy, generate better solutions than the coefficient between Ant. 1 and the isolator, and β2 is the coupling best-so-far solution. Gaussian process surrogate models are then coefficient between the isolator and Ant. 2. The total induced current constructed for each candidate in each child population using the in Ant. 2 then becomes αI + β1β2I.
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