Neural-Network Based Self-Initializing Algorithm for Multi-Parameter Optimization of High-Speed ADCs

Bansal, Shrestha; Ghaderi, Erfan; Puglisi, Chase; Sk, Gupta

doi:10.1109/tcsii.2020.3012386

Cited by 21 publications

(5 citation statements)

References 20 publications

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“…Besides synchronization and large number of channels, repeated parameterized sweeps across frequencies and angles to characterize a multi-antenna transceiver undertakes significant efforts without closed-loop automation in place. In [23], the TTD SSP (in particular, TDC and the time amplifiers) were optimized using particle swarm optimization techniques from [86]. While the optimization was limited to TDC only and not the entire SSP, this method shows the potential opportunities to further extend the test bench automation for closed-loop signal path optimization.…”

Section: B Multi-antenna Testbed and Validationmentioning

confidence: 99%

Wideband Beamforming with Rainbow Beam Training using Reconfigurable True-Time-Delay Arrays for Millimeter-Wave Wireless

Lin¹,

Boljanovic²,

Han³

et al. 2021

Preprint

Self Cite

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The decadal research in integrated true-time-delay arrays have seen organic growth enabling realization of wideband beamformers for large arrays with wide aperture widths. This article introduces highly reconfigurable delay elements implementable at analog or digital baseband that enables multiple Spatial Signal Processing (SSP) functions including wideband beamforming, wideband interference cancellation, and fast beam training. Details of the beam-training algorithm, system design considerations, system architecture and circuits with large delay range-to-resolution ratios are presented leveraging integrated delay compensation techniques. The article lays out the framework for true-time-delay based arrays in next-generation network infrastructure supporting 3D beam training in planar arrays, low latency massive multiple access, and emerging wireless communications standards.

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Section: B Multi-antenna Testbed and Validationmentioning

confidence: 99%

Wideband Beamforming with Rainbow Beam Training using Reconfigurable True-Time-Delay Arrays for Millimeter-Wave Wireless

Lin¹,

Boljanovic²,

Han³

et al. 2021

Preprint

Self Cite

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“…Recent works demonstrate that Artificial Intelligence (AI) algorithms can be applied to automate, or to assist, analog circuit design [11]- [19]. Their use in Σ∆Ms has been employed to improve the performance metrics of Σ∆Ms and other ADCs by means of linearization or calibration techniques based on Artificial Neural Networks (ANNs) [20], [21]. Some authors have proposed using ANNs in an optimization-based synthesis methodology [11]- [13], [15], [20], [22].…”

Section: Introductionmentioning

confidence: 99%

“…Their use in Σ∆Ms has been employed to improve the performance metrics of Σ∆Ms and other ADCs by means of linearization or calibration techniques based on Artificial Neural Networks (ANNs) [20], [21]. Some authors have proposed using ANNs in an optimization-based synthesis methodology [11]- [13], [15], [20], [22]. In some of them, the ANN has been trained to replace the simulator, while other approaches consider ANNs as an optimization engine.…”

Section: Introductionmentioning

confidence: 99%

On the Use of Artificial Neural Networks for the Automated High-Level Design of ΣΔ Modulators

Díaz-Lobo,

Liñán-Cembrano,

de la Rosa

2024

IEEE Trans. Circuits Syst. I

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This paper presents a high-level synthesis methodology for Sigma-Delta Modulators (Σ∆Ms) that combines behavioral modeling and simulation for performance evaluation, and Artificial Neural Networks (ANNs) to generate high-level designs variables for the required specifications. To this end, comprehensive datasets made up of design variables and performance metrics, generated from accurate behavioral simulations of different kinds of Σ∆Ms, are used to allow the ANN to learn the complex relationships between design-variables and specifications. Several representative case studies are considered, including single-loop and cascade architectures with single-bit and multi-bit quantization, as well as both Switched-Capacitor (SC) and Continuous-Time (CT) circuit techniques. The proposed solution works in two steps. First, for a given set of specifications, a trained classifier proposes one of the available Σ∆M architectures in the dataset. Second, for the proposed architecture, a Regression-type Neural Network (RNN) infers the design variables required to produce the requested specifications. A comparison with other optimization methods -such as genetic algorithms and gradient descent -is discussed, demonstrating that the presented approach yields to more efficient design solutions in terms of performance metrics and CPU time.

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“…Some scholars make a combination of optimization algorithms and ANNs. The hyperparameters are firstly fixed by optimization algorithms, such as genetic algorithm (GA) and particle swarm optimization (PSO), and then the data would be trained by the neural network [23][24][25]. The strategy above allows to improved the prediction accuracy to some extent.…”

Section: Introductionmentioning

confidence: 99%

A New Approach for the 10.7-cm Solar Radio Flux Forecasting: Based on Empirical Mode Decomposition and LSTM

Luo¹,

Zhu²,

Zhu³

et al. 2021

IJCIS

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The daily 10.7-cm Solar Radio Flux (F10.7) data is a time series with highly volatile. The accurate prediction of F10.7 has a great significance in the fields of aerospace and meteorology. At present, the prediction of F10.7 is mainly carried out by linear models, nonlinear models, or a combination of the two. The combination model is a promising strategy, which attempts to benefit from the strength of both. This paper proposes an Empirical Mode Decomposition (EMD) -Long Short-Term Memory Neural Network (LSTMNN) hybrid method, which is assembled by a particular frame, namely EMD-LSTM. The original dataset of F10.7 is firstly processed by EMD and decomposed into a series of components with different frequency characteristics. Then the output values of EMD are respectively fed to a developed LSTM model to acquire the predicted values of each component. The final forecasting values are obtained after a procedure of information reconstruction. The evaluation is undertaken by some statistical evaluation indexes in the cases of 1-27 days ahead and different years. Experimental results show that the proposed method gives superior accuracy as compared with benchmark models, including other isolated algorithms and hybrid methods.

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Neural-Network Based Self-Initializing Algorithm for Multi-Parameter Optimization of High-Speed ADCs

Cited by 21 publications

References 20 publications

Wideband Beamforming with Rainbow Beam Training using Reconfigurable True-Time-Delay Arrays for Millimeter-Wave Wireless

Wideband Beamforming with Rainbow Beam Training using Reconfigurable True-Time-Delay Arrays for Millimeter-Wave Wireless

On the Use of Artificial Neural Networks for the Automated High-Level Design of ΣΔ Modulators

A New Approach for the 10.7-cm Solar Radio Flux Forecasting: Based on Empirical Mode Decomposition and LSTM

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