2,141 research outputs found
State-of-the-art: AI-assisted surrogate modeling and optimization for microwave filters
Microwave filters are indispensable passive devices for modern wireless communication systems. Nowadays, electromagnetic (EM) simulation-based design process is a norm for filter designs. Many EM-based design methodologies for microwave filter design have emerged in recent years to achieve efficiency, automation, and customizability. The majority of EM-based design methods exploit low-cost models (i.e., surrogates) in various forms, and artificial intelligence techniques assist the surrogate modeling and optimization processes. Focusing on surrogate-assisted microwave filter designs, this article first analyzes the characteristic of filter design based on different design objective functions. Then, the state-of-the-art filter design methodologies are reviewed, including surrogate modeling (machine learning) methods and advanced optimization algorithms. Three essential techniques in filter designs are included: 1) smart data sampling techniques; 2) advanced surrogate modeling techniques; and 3) advanced optimization methods and frameworks. To achieve success and stability, they have to be tailored or combined together to achieve the specific characteristics of the microwave filters. Finally, new emerging design applications and future trends in the filter design are discussed
An Overview on Application of Machine Learning Techniques in Optical Networks
Today's telecommunication networks have become sources of enormous amounts of
widely heterogeneous data. This information can be retrieved from network
traffic traces, network alarms, signal quality indicators, users' behavioral
data, etc. Advanced mathematical tools are required to extract meaningful
information from these data and take decisions pertaining to the proper
functioning of the networks from the network-generated data. Among these
mathematical tools, Machine Learning (ML) is regarded as one of the most
promising methodological approaches to perform network-data analysis and enable
automated network self-configuration and fault management. The adoption of ML
techniques in the field of optical communication networks is motivated by the
unprecedented growth of network complexity faced by optical networks in the
last few years. Such complexity increase is due to the introduction of a huge
number of adjustable and interdependent system parameters (e.g., routing
configurations, modulation format, symbol rate, coding schemes, etc.) that are
enabled by the usage of coherent transmission/reception technologies, advanced
digital signal processing and compensation of nonlinear effects in optical
fiber propagation. In this paper we provide an overview of the application of
ML to optical communications and networking. We classify and survey relevant
literature dealing with the topic, and we also provide an introductory tutorial
on ML for researchers and practitioners interested in this field. Although a
good number of research papers have recently appeared, the application of ML to
optical networks is still in its infancy: to stimulate further work in this
area, we conclude the paper proposing new possible research directions
PRIOR KNOWLEDGE BASED NEURAL MODELING OF MICROSTRIP COUPLED RESONATOR FILTERS
The design of microstrip coupled resonator filters includes determination of the coupling coefficients between the filter resonator units. In this paper a novel modeling procedure exploiting prior knowledge neural approach is proposed as an efficient alternative to the standard electromagnetic (EM) simulations and to the neural models based purely on the artificial neural networks (ANNs). It has similar accuracy as the EM simulations and requires less training data and less time needed for the model development than the models based purely on ANNs
Power-Ground Plane Impedance Modeling Using Deep Neural Networks and an Adaptive Sampling Process
This paper proposes a deep neural network (DNN) based method for the purpose of power-ground plane impedance modeling. A composite DNN model, which is a combination of two DNNs is used to predict the Z-parameters of power ground planes from their design parameters. The first DNN predicts the normalized Z-parameters whereas the second DNN predicts the original maximum and minimum values of the non-normalized Z-parameters. This allows the method to retain a high accuracy when predicting responses that have large variations across designs, as is the case with the Z-parameters of the power-ground planes. We use the adaptive sampling algorithm to generate the training and validation samples for the DNNs. The adaptive sampling algorithm starts with only a few samples, then slowly generates more samples in the non-linear regions within the design parameters space. The level of non-linearity of the regions is determined by a surrogate model which is also trained using the generated samples as well. If the surrogate model has poor prediction accuracy in a region, then the adaptive sampling algorithm will generate more samples in that region. A shallow neural network is used as the surrogate model for non-linearity determination of the regions since it is faster to train and update. Once all the samples have been generated, they will be used to train and validate the composite DNN models. Finally, we present two examples, a square-shaped power ground plane and a square-shaped power ground plane with a hollow square at the center to demonstrate the robustness of the DNN composite models
Power-Ground Plane Impedance Modeling Using Deep Neural Networks and an Adaptive Sampling Process
This paper proposes a deep neural network (DNN) based method for the purpose of power-ground plane impedance modeling. A composite DNN model, which is a combination of two DNNs is used to predict the Z-parameters of power ground planes from their design parameters. The first DNN predicts the normalized Z-parameters whereas the second DNN predicts the original maximum and minimum values of the non-normalized Z-parameters. This allows the method to retain a high accuracy when predicting responses that have large variations across designs, as is the case with the Z-parameters of the power-ground planes. We use the adaptive sampling algorithm to generate the training and validation samples for the DNNs. The adaptive sampling algorithm starts with only a few samples, then slowly generates more samples in the non-linear regions within the design parameters space. The level of non-linearity of the regions is determined by a surrogate model which is also trained using the generated samples as well. If the surrogate model has poor prediction accuracy in a region, then the adaptive sampling algorithm will generate more samples in that region. A shallow neural network is used as the surrogate model for non-linearity determination of the regions since it is faster to train and update. Once all the samples have been generated, they will be used to train and validate the composite DNN models. Finally, we present two examples, a square-shaped power ground plane and a square-shaped power ground plane with a hollow square at the center to demonstrate the robustness of the DNN composite models
34th Midwest Symposium on Circuits and Systems-Final Program
Organized by the Naval Postgraduate School Monterey California. Cosponsored by the IEEE Circuits and Systems Society.
Symposium Organizing Committee: General Chairman-Sherif Michael, Technical Program-Roberto Cristi, Publications-Michael Soderstrand, Special Sessions- Charles W. Therrien, Publicity: Jeffrey Burl, Finance: Ralph Hippenstiel, and Local Arrangements: Barbara Cristi
System level power integrity transient analysis using a physics-based approach
With decreasing supply voltage level and massive demanding current on system chipset, power integrity design becomes more and more critical for system stability. The ultimate goal of well-designed power delivery network (PDN) is to deliver desired voltage level from the source to destination, in other words, to minimize voltage noise delivered to digital devices. The thesis is composed of three parts. The first part focuses on-die level power models including simplified chip power model (CPM) for system level analysis and the worst scenario current profile. The second part of this work introduces the physics-based equivalent circuit model to simplify the passive PDN model to RLC circuit netlist, to be compatible with any spice simulators and tremendously boost simulation speed. Then a novel system/chip level end-to-end transient model is proposed, including the die model and passive PDN model discussed in previous two chapters as well as a SIMPLIS based small signal VRM model. In the last part of the thesis, how to model voltage regulator module (VRM) is explicitly discussed. Different linear approximated VRM modeling approaches have been compared with the SIMPLIS small signal VRM model in both frequency domain and time domain. The comparison provides PI engineers a guideline to choose specific VRM model under specific circumstances. Finally yet importantly, a PDN optimization example was given. Other than previous PDN optimization approaches, a novel hybrid target impedance concept was proposed in this thesis, in order to improve system level PDN optimization process --Abstract, page iv
Machine Learning for Metasurfaces Design and Their Applications
Metasurfaces (MTSs) are increasingly emerging as enabling technologies to
meet the demands for multi-functional, small form-factor, efficient,
reconfigurable, tunable, and low-cost radio-frequency (RF) components because
of their ability to manipulate waves in a sub-wavelength thickness through
modified boundary conditions. They enable the design of reconfigurable
intelligent surfaces (RISs) for adaptable wireless channels and smart radio
environments, wherein the inherently stochastic nature of the wireless
environment is transformed into a programmable propagation channel. In
particular, space-limited RF applications, such as communications and radar,
that have strict radiation requirements are currently being investigated for
potential RIS deployment. The RIS comprises sub-wavelength units or meta-atoms,
which are independently controlled and whose geometry and material determine
the spectral response of the RIS. Conventionally, designing RIS to yield the
desired EM response requires trial and error by iteratively investigating a
large possibility of various geometries and materials through thousands of
full-wave EM simulations. In this context, machine/deep learning (ML/DL)
techniques are proving critical in reducing the computational cost and time of
RIS inverse design. Instead of explicitly solving Maxwell's equations, DL
models learn physics-based relationships through supervised training data. The
ML/DL techniques also aid in RIS deployment for numerous wireless applications,
which requires dealing with multiple channel links between the base station
(BS) and the users. As a result, the BS and RIS beamformers require a joint
design, wherein the RIS elements must be rapidly reconfigured. This chapter
provides a synopsis of DL techniques for both inverse RIS design and
RIS-assisted wireless systems.Comment: Book chapter, 70 pages, 12 figures, 2 tables. arXiv admin note:
substantial text overlap with arXiv:2101.09131, arXiv:2009.0254
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