Digitally Assisted Analog/RF Predistorter With a Small-Signal-Assisted Parameter &lt;newline/&gt;Identification Algorithm

Huang, Hai; Xia, Jingjing; Islam, Anik; Ng, Eric; Levine, Peter M.; Boumaiza, Slim

doi:10.1109/tmtt.2015.2495362

Cited by 6 publications

(4 citation statements)

References 18 publications

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“…• To eliminate the factor of high sampling rates and hardware complexities of black-box signal processors, effective [33], [34], [175], [206]- [208] [31], [209], [210] [35], [36] Array-Based APD [57], [176], [177], [199] LUT-Based DPD [76], [133], [138], [180], [181] Adaptive DPD [39], [60], [66], [100], [134], [182]- [191] For Standalone PA [66], [194]- [196] For MIMO Systems [38], [197] [57], [68], [192], [197]- [200] [52], [109], [192], [193], [201]- [205] Fig. 7.…”

Section: Summary and Lessons Learnedmentioning

confidence: 99%

“…This subsection further covers the key contributions of HPD in different PA operating scenarios investigated in recent research works. 1) HPD Linearization for Standalone PA: This method provides a high level of nonlinearity compensation since it alleviates PA static and dynamic distortions [194]. This HPD testing mainly applies to low-frequency range signals (e.g., sub-6 GHz) as the preceding analog circuitry becomes lossy and bulky at the high-frequency range.…”

Section: Hybrid Predistortion (Hpd)mentioning

confidence: 99%

“…On the other hand, the long-term MEs or cross-term polynomials, if treated properly in HPD, effectively linearize the broadband systems in compliance with the 5G-CA schemes [66], [194]. Likewise, in [66], an FPGA-based baseband is deployed to control the APD circuits for higher accuracy digitally.…”

Section: Hybrid Predistortion (Hpd)mentioning

confidence: 99%

“…Cellular BS Communications [194], [219] PA for linearization, which is not applicable in DPD that occupies the five-fold of sampling bandwidth to an operating band. Subsequently, to get maximum benefits from APD, one common principle is to ensure a careful shaping of drain and gate bias voltages [21], since it is complementary in principle to maintaining optimal instantaneous envelope of the RF carrier towards achieving satisfactory linearity.…”

Section: Predistortion Case Studiesmentioning

confidence: 99%

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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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Section: Summary and Lessons Learnedmentioning

confidence: 99%

Section: Hybrid Predistortion (Hpd)mentioning

confidence: 99%

Section: Hybrid Predistortion (Hpd)mentioning

confidence: 99%

Section: Predistortion Case Studiesmentioning

confidence: 99%

See 2 more Smart Citations

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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A low-power high-performance digital predistorter for wideband power amplifiers

Manyam

Pham

Jabbour

et al. 2018

Analog Integr Circ Sig Process

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Behavioral Modeling of GaN Doherty Power Amplifiers Using Memoryless Polar Domain Functions and Deep Neural Networks

et al. 2020

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In this paper, novel Doherty Power Amplifier (DPA) models are presented. The motivation behind the proposed models is to accurately predict static nonlinearities in the compression regions of the carrier and peaking amplifiers. DPAs suffer from a nonlinearity that originates from the carrier amplifier, and a second more pronounced nonlinearity generated at the full compression region following the turn-on of the peaking amplifier. Moreover, these distortions are often observed at different input power levels depending on whether the AM-AM or the AM-PM characteristic is considered. Therefore, the proposed static model is based on independent modeling of the memoryless gain in the polar domain. The static model of the memoryless AM-AM and AM-PM characteristics is augmented with either memory polynomials or deep neural network functions for memory effects modeling. The methodology of building the proposed models and the achieved results are discussed in this paper. The MP based proposed model achieves an NMSE as low as 45.3 − dB with only 78 model parameters, while the DNN based model achieves an NMSE as low as 46.1 − dB with only 156 model parameters. However, the DNN based model achieves the best model resilience to changes in the identification data.

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Digitally Assisted Analog/RF Predistorter With a Small-Signal-Assisted Parameter <newline/>Identification Algorithm

Cited by 6 publications

References 18 publications

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

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

A low-power high-performance digital predistorter for wideband power amplifiers

Behavioral Modeling of GaN Doherty Power Amplifiers Using Memoryless Polar Domain Functions and Deep Neural Networks

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