Sampling Rate Reduction for Digital Predistortion of Broadband RF Power Amplifiers

Li, Yue; Wang, Xiaoyu; Zhu, Anding

doi:10.1109/tmtt.2019.2944813

Cited by 38 publications

(20 citation statements)

References 25 publications

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“…Furthermore, as we target at wideband DPD solutions, the bandwidth limit for data converters is also a practical issue in real-world applications [4]. Some recent works have focused on this problem and proposed some promising solutions [48], [49]. We believe that the combination of this work with these techniques can be a good topic for future research.…”

Section: Model Extraction Complexitymentioning

confidence: 97%

Boosted Model Tree-Based Behavioral Modeling for Digital Predistortion of RF Power Amplifiers

Wang

Pang

et al. 2021

IEEE Trans. Microwave Theory Techn.

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In this article, we propose a new behavioral modeling approach, called boosted model tree, to characterize and compensate for the complex nonlinear distortions induced by wideband high-efficiency radio frequency power amplifiers. With the proposed model, the input data are classified into different zones by decision trees and each zone is assigned separate submodels. We also employ a model boosting technique to build multiple parallel tree structures that jointly model the desired nonlinear behavior. By designing dedicated optimization procedures, both tree structures and submodel coefficients can be efficiently identified. It is demonstrated that the combination of piecewise and parallel structures provides a powerful and hardware-efficient way to model nonlinear memory effect and cross terms. Based on the experimental results, the proposed method can achieve improved linearization performance with low hardware complexity under challenging wideband predistortion scenarios.

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Section: Model Extraction Complexitymentioning

confidence: 97%

Boosted Model Tree-Based Behavioral Modeling for Digital Predistortion of RF Power Amplifiers

Wang

Pang

et al. 2021

IEEE Trans. Microwave Theory Techn.

Self Cite

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“…On the other side, the 5G-CA schemes have imposed major limitations on DPD wideband running capabilities, especially in targeting nonlinearities of the gigabit broadband spectrum. In this way, it is important to determine an appropriate rate of sampling bandwidth in the presence of memory effects (MEs) to meet the target performance of intra-band (within carrier), and inter-band (outside carrier) signal modulations [28], [29]. It is also worth highlighting that the worst-case scenario of ambient MEs occurs in the wideband systems, where the inter-band distortion coincides with DPD modulated intra-band signal [15], [30].…”

Section: Introductionmentioning

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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“…Basis functions within a DPD model can be selectively partitioned, and updated separately. The rationale of updating only a portion of the DPD coefficients is that all coefficients when calculated do not converge uniformly over training iterations [7]. The Frisch-Waugh-Lovell (FWL) theorem [8] can be applied to allow a local update of a single partition while the remaining coefficients were held constant.…”

Section: Introductionmentioning

confidence: 99%

Acceleration of Digital Pre- Distortion Training Using Selective Partitioning

Loughman

Byrne

Farrell

et al. 2022

2022 IEEE Topical Conference on RF/Microwave Power Amplifiers for Radio and Wireless Applications (PAWR)

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In recent years model and Digital Pre-Distortion dimension reduction has been widely researched. The operations involved when running DPD are often far less than those needed during the training of the DPD coefficients. The proposed partitioned Least Squares (LS) adaptation allows a selected subset of DPD coefficients to be updated while the remaining coefficients are held constant. This technique allows a more adaptive training procedure, improved interpretability of the important DPD coefficient's during training and the ability to partition the DPD function into specific groups. The Frisch-Waugh-Lovell (FWL) theorem is exploited to partition the coefficients of a DPD basis function trained using LS regression. The proposed methodology was experimentally validated with a Generalized Memory Polynomial (GMP) DPD function, used to linearize a 5W power amplifier (PA) driven by a 40MHz 5G-NR signal.

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Sampling Rate Reduction for Digital Predistortion of Broadband RF Power Amplifiers

Cited by 38 publications

References 25 publications

Boosted Model Tree-Based Behavioral Modeling for Digital Predistortion of RF Power Amplifiers

Boosted Model Tree-Based Behavioral Modeling for Digital Predistortion of RF Power Amplifiers

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

Acceleration of Digital Pre- Distortion Training Using Selective Partitioning

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