Linearization of mm-Wave Large-Scale Phased Arrays Using Near-Field Coupling Feedback for &gt;10Gb/s Wireless Communication

Murugesu, Rosanah; Holyoak, Michael J.; Chow, Hungkei; Shahramian, Shahriar

doi:10.1109/ims30576.2020.9223967

Cited by 8 publications

(6 citation statements)

References 12 publications

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“…Neural Network-Based Model Waveform Processing at Transmission and Observation Path [67], [81], [89]- [103] [110]- [120], [122], [123] [67], [75], [82]- [88] DAC and ADC nonidealities [29], [130], [133]- [141] RF Sub-Components Effects [130]- [132], [142], [145] [130], [131] Amplitude-Compensated Phase Shifter [78], [79], [148]- [155], [157], [158] Phase-Compensated and Low-Noise Variable Gain Amplifier [44], [149], [159]- [169] SVM-Based Model better output performance. Since the invention of wireless communication, the linearity of transmitting signals hold paramount importance [73], [74].…”

Section: Baseband Waveform Processing Volterra Series-based Modelmentioning

confidence: 99%

“…processing complexity increases accordingly with the spectrum resources; for example, an operating signal of 2.5-Gbaud/s with 500-MHz bandwidth signal in 384-elements mMIMO TRX can easily compass the baseband data rates to 10-Gbaud/s in 16 QAM signal [82] (which can be even higher as the constellation modulation grows). In other words, the baseband sampling frequency with the data rates of typically 5× or 7× higher than the input Nyquist modulation bandwidth is needed to predistort the IB and OOB distortions.…”

Section: A Baseband Waveform Processingmentioning

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: Baseband Waveform Processing Volterra Series-based Modelmentioning

confidence: 99%

Section: A Baseband Waveform Processingmentioning

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

View full text Add to dashboard Cite

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“…Two signals, z 1 and z 2 , are used in the input and two outputs, y 1 and y 2 , are captured via OTA measurements, e.g., OTA diversity feedbacks [21]. Alternative methods, including coupler-based combined feedback [22], embedded antennas [23], [24], and common feedback line [25], can be used as well.…”

Section: Model Extractionmentioning

confidence: 99%

Linearization Angle Widened Digital Predistortion for 5G MIMO Beamforming Transmitters

Luo

Zhu

et al. 2021

IEEE Trans. Microwave Theory Techn.

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To reduce the demand for frequently adjusting phase shifts in the fifth-generation (5G) multiple-input multipleoutput (MIMO) beamforming transmitters, in this article, a novel digital predistortion (DPD) technique is proposed, which employs multiple predistortion boxes to shape the pattern of nonlinear distortions in space in order to linearize multiple targets simultaneously. By using this approach, the linearization angle is widened and thus the communication quality can be kept when the user moves off the main beam direction. As a proof of concept, two DPD boxes, namely main and auxiliary boxes, are used to realize the 2-target linearization. A new model extraction method is also proposed with a dedicated over-the-air (OTA) feedback postprocessing stage. Simulation and experiment are demonstrated on a 4-path beamforming transmitter. The results show that the proposed DPD method can effectively widen the linearization angle with reasonable additional complexity.

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“…To update the DPD model coefficients, different data acquisition strategies have been proposed. The desired target signal for DPD can be captured using embedded antennas [10], [11], dedicated coupling structures [12] or using OTA antennas [13], [14]. To avoid interruption of data transmission, Wang et al [15], [16] proposed the OTA-based indirect identification technique.…”

mentioning

confidence: 99%

Digital Predistortion of 5G Multiuser MIMO Transmitters Using Low-Dimensional Feature-Based Model Generation

Wang

Yin

et al. 2022

IEEE Trans. Microwave Theory Techn.

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In this article, we present a novel digital predistortion (DPD) system which can be updated quickly and efficiently in response to the dynamic reconfiguration of multiuser multipleinput multiple-output (MIMO) transmitters. By identifying the shared properties of different power amplifiers (PAs) with two feature extraction stages, nonlinear behaviors of the PAs are encoded into low-dimensional features. Using the extracted features as input, a novel DPD generator is employed to synthesize DPD model coefficients directly. In this way, when the power levels or beam angles change, the DPD model can be updated fast and accurately without capturing the output data or applying linear system identification algorithms. To capture slow-varying dynamics of the PAs, the features can also be calibrated efficiently in the background. Computational complexity and operational latency can thus be reduced significantly. Simulation and experimental results demonstrate that the proposed DPD approach achieves excellent linearization performance with low complexity, making itself a promising linearization solution for 5G massive MIMO transmitters.

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Linearization of mm-Wave Large-Scale Phased Arrays Using Near-Field Coupling Feedback for >10Gb/s Wireless Communication

Cited by 8 publications

References 12 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

Linearization Angle Widened Digital Predistortion for 5G MIMO Beamforming Transmitters

Digital Predistortion of 5G Multiuser MIMO Transmitters Using Low-Dimensional Feature-Based Model Generation

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