116 research outputs found
Combinational of surrogate modeling and particle swarm optimization for improving the electromagnetic performances of a frequency selective surface
No full textFrequency-selective surfaces (FSSs) consist of the repetition of unit cells for controlling reflection, transmission/absorption of electromagnetic (EM) fields. They are typically employed at radio and optical frequencies. Simulation of such large (in terms of wavelength) structures based on the traditional EM simulations is time-consuming and requires significant computational resources. Hence, this paper devotes to present an optimization-oriented methodology for designing and optimizing FSS in an automated fashion. The FSS structure is optimized using the artificial neural network paradigm, where the particle swarm optimization is applied for sizing the design parameters. The optimization process is an automatic one where electronic design automation tool with numerical analyser is working together, leading to effectively optimize the FSS design. To verify the effectiveness of the proposed method, an FSS structure exhibiting a wide transmission band for normal incidence in the 7.0-11.2 GHz range is considered
A Case Study for Improving Performance of Frequency Selective Surface through Union of Sub-Sets and Particle Swarm Optimization
No full textFrequency Selective Surfaces (FSSs) consist of a repetition of a given pattern in a periodic way; typically, a dielectric substrate supports this arrangement giving rise to a two-dimensional array. Although relatively simple in structure, designing an FSS that exhibits large bandwidth and stable response to oblique incidence is not straightforward and requires special attention and significant computational effort. To address this problem, this study presents a methodology whereby an initial configuration of the FSS pattern is subjected to an optimization method for sizing the geometrical parameters. Consequently, the initial unit cell is first broken down into subsections, specifically as a “union of subsets”, then particle swarm optimization is used to achieve optimal design parameters that further improves the overall FSS performances. To validate the proposed method, an X-band FSS is proposed and optimized in a commercial simulation environment (Microwave Studio, Dassault Systèmes)
Prediction of Class-Amplifiers with the Aid of Neural Network
No full textThis paper presents a strategy addressing the problem of selection of the class of the amplifiers to be used in future wireless communication systems. The proposed methodology uses a scheme based on neural networks (NN): the characteristics of each class of amplifier (i.e., A, B, AB, C, D, F, G, J, S, T , etc.) are determined and then the ‘classification NN’ is constructed for distinguishing various classes from each other. To validate the method, firstly the designs of various class-amplifiers are collected from the recently published literature, and then the specifications of the amplifiers are extracted in terms of voltage (V), current (I) and efficiency; finally with these data the classification NN is trained. After building this black-box NN, providing the required specifications of each amplifier, designer are informed about the class of amplifier that is predicated by the classification NN and that better fits the characteristics of the considered application. This methodology is important as it leads the way of amplifier class selection in the complex communication systems
Fully Automated Inside Body WDT Transmitter Design and Optimization Through Artificial Intelligence-Based GANs and DNNs
No full textBiomedical inside body wireless data transfer (WDT) interface includes the design of power amplifiers (PAs) with implantable antenna leading to operate concurrently. Hence, active and passive devices are utilized simultaneously for which the accurate starting points for designing these high dimensional devices is critical. From another point of view, accelerating the design and optimization process is another substantial issue that must be considered effectively. In this study, we propose a methodology that includes two optimization phases that are applied sequentially. In the first phase, the PA is designed and optimized by employing a generative adversarial network (GAN) for predicting the load-pull contours on the Smith chart and using a long short-term memory (LSTM)-based deep neural network (DNN) for achieving the optimal design parameters of the biomedical amplifier. In this step, the GAN leads to predicting the optimal impedances needed to construct the initial structure of PA through a simplified real frequency technique. In the second optimization phase, the initial structure of the biomedical antenna is constructed automatically by developing a visual basic (VBA) environment, then like the PA, the design parameters of the antenna are optimized through the LSTM-based DNN. Finally, another GAN is generated for predicting the radiation patterns of the antenna. In both phases, a multi-objective ant lion optimizer (MOALO) is employed in the output layer of DNNs for optimizing various outcome specifications. The proposed method is performed fully automatically: active and passive devices are designed and optimized with the help of GANs and DNNs in which the drawback of heavy reliance of the system performance on the designer's experience is solved in a fast way. The proposed method is validated by designing and optimizing a biomedical PA with an antenna working at the center frequency of 2.45 GHz which shows reliable outcomes
Beam-Steering Antenna Technique Using Operational Amplifiers for Sub-6 GHz
No full textA methodology for beam-steering using operational amplifiers (Op-amps) is presented. Continuous steering is required in various advanced applications and its implementation necessitates additional efforts since singularly control of the feeding of the radiators is mandatory for both phase and amplitude. The present work proposes a technique for generating the required sequence of the feeding signal using two Op-amps for each input port. It leads to generating incremental phases with sequential Op-amps without any limitation in the value of the phase differences, controlled by the bias voltage applied to the Op-amps, hence giving rise to a continuous beam-steering capability. The study case consists of a four-stage oscillator designed for creating a sequence of signals with progressive phase shifts between consecutive outputs. The general scheme allows continuous control of the phase differences here applied for generating a uniform, i.e., constant signal magnitude, feeding sequence. This set of signals is then used to feed a four-element microstrip array operating at 1.2 GHz. The effectiveness of the method is validated by numerical simulation of the array performances. Additionally, the low power consumption of active Op-amps, easy implementation, and high sensitivity are characteristics of the presented paradigm
Surrogate Modeling for Designing and Optimizing MIMO Antennas
No full textThis papers presents the design and optimization of multiple-input and multiple-output (MIMO) antennas through intelligent methods namely as: surrogate modeling. The optimization process is performed automatically with the combination of Microwave Studio (Dassault Systèmes) and MATLAB numerical analyzer. The proposed optimization method aims to find the optimal solution for the total active reflection coefficient (TARC) specification, S 11 , and S 12 by using shallow neural network. This methodology leads to efficiently size the design parameters of MIMO antenna and to optimize S-parameters and TARC specification jointly. To validate the proposed method, an ultra wideband MIMO antenna in the frequency band of 3.1 GHz to 10.6 GHz is designed and optimized
Conjointly active and passive modelings with deep neural networks as fully automated optimizations for upper-mid band 6G communications
No full textToday wireless systems include the fifth and sixth generations (5G and 6G) technologies and are growing day by day that result in exponentially increasing data traffic. For providing a reliable and high performance radio frequency (RF) designs especially for 6G networks, amplifiers and antenna as active and passive components play important roles. In the 5G/6G communication systems, the propagation loss is considerably large and its compensation requires high output power generated from the amplifiers for guaranteeing the satisfied quality of transmitted signal. From another point of view, the installed antennas must be able to optimally manage the radiated signals and handle/compensate nonlinear performances of the RF circuitry. Hence, advanced modeling and multi-objective optimization algorithms are required for designing and optimizing high performance amplifiers and antennas in terms of output power, gain, efficiency, linearity, and bandwidth. Concurrently optimizing active and passive components is not straightforward and typically it requires additional efforts by the RF designers. To tackle this drawback, a two-step methodology is proposed: (1) configuring the initial structure of active and passive devices, and (2) sizing the configured devices. In this work, various methods are introduced for structuring the topology of circuits and then artificial intelligence, including machine learning and neural networks, is preferred among other surrogate modelling for sizing the designs. These neural networks are satisfied due to the accurate modeling responses and are able to provide an automated optimization process leads to employ multi-objective optimization methods. In this work, an automated optimization process for comprehensive design of high-performance amplifiers with antennas through bottom-up optimization (BUO) method and long short-term memory (LSTM)-based deep neural networks (DNNs) is proposed. At the output layer of DNNs, the multi-objective multi-verse optimizer (MOMVO) method is employed for optimizing various specifications of active device (i.e., amplifier), and passive device (i.e., antenna), concurrently. In the presented method, all the electromagnetic (EM) design rules are implemented which results in reducing simulation time in the harmonic balance simulation environment that also provides ready to fabricate layouts. The novelty consists of the all-inclusive style that (1) reduces the manual breaks, aka time-to-market, and (2) delivers ready-to-fabricate layouts of the device that exhibits global optimum performances, automatically. The validation of the proposed method is verified by designing and optimizing high power amplifier (HPA) with antenna in the frequency band from 9.0 GHz to 9.6 GHz, suitable for upper-mid band 6G communications
On the Role of Artificial Intelligent Technology for Millimetre-Wave and Terahertz Applications
No full textNext-generation wireless communication networks are developing across the world day by day; this requires high data rate transportation over the systems. Millimeter-wave (mm-wave) spectrum with terahertz (THz) bands is a promising solution for next-generation systems that are able to meet these requirements effectively. For such networks, designing new waveforms, providing high-quality service, reliability, energy efficiency, and many other specifications are taking on important roles in adapting to high-performance communication systems. Recently, artificial intelligence (AI) and machine learning (ML) methods have proved their effectiveness in predicting. and optimizing nonlinear characteristics of high-dimensional systems with enhanced capability along with rich convergence outcomes. Thus, there is a strong need for the use of these intelligence-based methods to achieve higher bandwidths along with the targeted outcomes in comparison with the traditional designs. In this work, we provide an overview of the recently published works on the utilization of mm-wave and THz frequencies for designing and implementing various designs to carry out the targeted key specifications. Moreover, by considering various newly published works, some open challenges are identified. Hence, we provide our view about these concepts, which will pave the way for readers to get a general overview and ideas around the various mm-wave and THz-based designs with the use of AI methods
Nonlinear Behavioral Modeling of FETs: Toward the Implementation of Deep Neural Networks Through Large Signal Data and EDA Tools
No full textNonlinear behavioral Field-Effect Transistor (FET) models often rely on large look-up tables extracted from extensive load-pull characterization. Besides the numerical burden, these models have limited extrapolation capabilities and can hardly be made dependent on the device technology. In this paper, we demonstrate that a Long Short-Term Memory (LSTM)-based Deep Neural Network (DNN) is an effective alternative modeling approach. The DNN is trained with the load-pull data within a simulation platform where data exchange between an Electronic Design Automation (EDA) tool (such as PathWave ADS) and a programming platform (such as MATLAB) is exploited. As a test case, the DNN model has been extracted for an S-band MACOM 10W GaN power device, for which the Enhanced Poly-Harmonic Distortion (EPHD) behavioral model is also available in the ADS. The accuracy of DNN model is verified against the EPHD model in terms of output power, gain, efficiency, and dynamic load lines. Compared to other behavioral models, the DNN approach is expected to provide superior extrapolation capability and to be easily reconfigurable to add/combine heterogeneous device data e.g. from advanced characterization, including memory, and physical (TCAD, EM) simulations
Patch Antenna Array Design through Bottom-Up and Bayesian Optimizations
No full textThis paper presents the design and the optimization of a 2 × 2 antenna array through two optimization techniques, namely bottom-up optimization (BUO) and Bayesian optimization (BO). These sequential optimizations tackle the difficulty problem of antenna array design by predicting the suitable number of single radiators and by sizing the configuration of feeding circuit using BUO and BO methods, respectively. The proposed method leads to the design of high performance antenna arrays in terms of bandwidth, gain and radiated patterns where all the overall optimization process is performed with the combination of electronic design automation tool and a numerical analyzer. To validate the proposed method, a patch antenna array is designed and optimized in a frequency band from 12.9 GHz to 13.7 GHz with the maximum gain of 17.9 dB in the considered band
- …
