Beyond the Moore-Penrose Inverse: Strategies for the Estimation of Digital Predistortion Linearization Parameters

Gilabert, Pere L.; Braithwaite, R. Neil; Montoro, Gabriel

doi:10.1109/mmm.2020.3023220

Cited by 17 publications

(5 citation statements)

References 36 publications

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“…As a variant of the ANN, the ELM does not require gradient-based backpropagation to adjust the weights but sets the weights through the Moore-Penrose generalized inverse [20]. The standard ELM neural network structure is shown in Fig.…”

Section: Related Work 21 Extreme Learning Machinementioning

confidence: 99%

An improved Kalman particle swarm optimization for modeling and optimizing of boiler combustion characteristics

et al. 2022

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With the rapid development of the national economy, the demand for electricity is also growing. Thermal power generation accounts for the highest proportion of power generation, and coal is the most commonly used combustion material. The massive combustion of coal has led to serious environmental pollution. It is significant to improve energy conversion efficiency and reduce pollutant emissions effectively. In this paper, an extreme learning machine model based on improved Kalman particle swarm optimization (ELM-IKPSO) is proposed to establish the boiler combustion model. The proposed modeling method is applied to the combustion modeling process of a 300 MWe pulverized coal boiler. The simulation results show that compared with the same type of modeling method, ELM-IKPSO can better predict the boiler thermal efficiency and NOx emission concentration and also show better generalization performance. Finally, multi-objective optimization is carried out on the established model, and a set of mutually non-dominated boiler combustion solutions is obtained.

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Section: Related Work 21 Extreme Learning Machinementioning

confidence: 99%

An improved Kalman particle swarm optimization for modeling and optimizing of boiler combustion characteristics

et al. 2022

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“…While linear-in-parameter models, such as GMP-based piecewise models, can be identified using a closed-form least-squares solution [39], NNs require iterative optimization techniques that use gradient estimates to converge the model parameters. In this work, we use the adaptive moment estimation (Adam) optimizer, which is an extension to the stochastic gradient descent algorithm, maintaining a per-parameter learning rate based on the first and second moments of the gradients [40].…”

Section: End-to-end Nn Trainingmentioning

confidence: 99%

Sparsely Gated Mixture of Experts Neural Network For Linearization of RF Power Amplifiers

Fischer-Bühner,

Brihuega,

Anttila

et al. 2024

IEEE Trans. Microwave Theory Techn.

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This article presents a piecewise neural network (NN) with dynamic sparsity for modeling and linearization of radio frequency (RF) power amplifiers (PAs). A mixture of experts NN (MENN) approach is employed to combine several smaller real-valued time-delay NNs (RVTDNNs) by means of a gating NN. Furthermore, we complement the MENN framework with top-K sparse gating, such that only a subset of experts is activated during each sample inference, reducing the computational complexity at run time. An end-to-end training approach is presented, to optimize the gating alongside with specializing the expert NNs, enabling the experts to collaborate. We experimentally investigate the scaleability of the proposed model in terms of modeling accuracy and linearization performance, as well as run time and model complexity, using RF measurements with two different gallium-nitride Doherty PAs at 1.8 and 3.5 GHz, respectively. Our experiments confirm a significant reduction in run-time complexity due to the sparse gating, with only a small penalty on accuracy, linearization capability and scaleability. Furthermore, the proposed approach is shown to offer favorable complexity-performance trade-offs, outperforming the existing state-of-the-art.

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“…These circumstances are well-arduous for a decent DPD system. Hence, two kinds of precautionary measures can be taken to alleviate the ill-conditioning issues in ADPD, that is 1) pruning of PD polynomial model to update relevant coefficient sets of basis function [183]- [185]; 2) applying regularization matrix to steady the training samples of weight vector especially in iterative online linearization [60], [100], [179], [186]- [188].…”

Section: ) Adaptive Dpd (Adpd)mentioning

confidence: 99%

Predistortion-Based Linearization for 5G and Beyond Millimeter-Wave Transceiver Systems: A Comprehensive Survey

Haider

You

et al. 2022

IEEE Commun. Surv. Tutorials

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The next-generation (5G/6G) wireless communication aims to leapfrog the currently occupied sub-6 GHz spectrum to the wideband millimeter-wave (MMW) spectrum. However, MMW spectrums with high-order modulation schemes drive the power amplifier (PA) at significant back-off, causing severe nonlinear distortions, thus deteriorating the transceiver's (TRXs) modulation process. Typically, the TRX efficacy is quantified with standardized linearization matrices, which take advantage of different predistortion (PD) schemes to handle deep compression of the PA. In this regard, TRX baseband signals are mostly linearized in the digital domain, where digitally controlled linearization needs higher sampling rates due to increasing MMW bandwidths to compensate for intermodulation (IMD) products, resulting in increased system cost and power consumption. Alternately, the digitally controlled analog-based linearization, i.e., the hybridization of digital predistortion (DPD) and analog predistortion (APD), is highly productive and cost-effective for fulfilling the linearized energy-efficient design vision of MMW networks. Therefore, this paper puts an extensive spotlight on the progress in PD-based linearization for 5G and beyond communications. It first provides background information on the advancements of PD schemes through recent surveys, then classifies the general roadmap of PD waveform processing across the TRX system models as preliminary. After this, we present three prominent PD architectures and their design approaches with intrinsic performance metrics. Finally, we explore four case studies encompassing PD operation under certain nonlinear constraints of different communication schemes. We examine the suitability of PD-based linearization solutions, both existing and proposed till the first quarter of 2022, and identify the potential prospects in this domain.

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Beyond the Moore-Penrose Inverse: Strategies for the Estimation of Digital Predistortion Linearization Parameters

Cited by 17 publications

References 36 publications

An improved Kalman particle swarm optimization for modeling and optimizing of boiler combustion characteristics

An improved Kalman particle swarm optimization for modeling and optimizing of boiler combustion characteristics

Sparsely Gated Mixture of Experts Neural Network For Linearization of RF Power Amplifiers

Predistortion-Based Linearization for 5G and Beyond Millimeter-Wave Transceiver Systems: A Comprehensive Survey

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