Stabilization of a Modified LMS Algorithm for Canceling Nonlinear Memory Effects

Xiao, Yue-Yu

doi:10.1109/tsp.2020.2997951

Cited by 7 publications

(3 citation statements)

References 35 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%

“…Meanwhile, the probability of instability due to the inevitable estimation errors during a longer estimate process or the mutual differences in the PD models is an important design challenge in providing LMS stability for realtime applications. Reference [191] expanded the study on LMS-based ADPD iteration to include signal-dependent noise (SDN) effects on the nonlinear memory-induced output weight vector. Since the SDN is accumulated in large iterations and causes divergence or matrix ill-conditioning, it is also well known from the authors' illustration that the reduced sampling rates are the initial source of SDN (or aliasing effects), as the cut-off frequency of the RX low-pass filter lies close to the first Nyquist zone.…”

Section: ) Adaptive Dpd (Adpd)mentioning

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: ) 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

View full text Add to dashboard Cite

show abstract

“…The straightforward steps of the least mean square (LMS) adaptive algorithm in weights update together with its fast convergence (if optimum step-size is selected), made it a very popular filtering algorithm. However, its convergence rate is easily affected by the spread of the eigenvalue of the autocorrelation matrix of the tap-input vector [9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Discrete-wavelet-transform recursive inverse algorithm using second-order estimation of the autocorrelation matrix

2020

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The recursive least squares (RLS) algorithm was introduced as an alternative to least mean square (LMS) algorithm with enhanced performance. Computational complexity and instability in updating the autocolleltion matrix are some of the drawbacks of the RLS algorithm that were among the reasons for the introduction of the second-order recursive inverse (RI) adaptive algorithm. The 2 nd order RI adaptive algorithm suffered from low convergence rate in certain scenarios that required a relatively small initial step-size. In this paper, we propose a new second-order RI algorithm that projects the input signal to a new domain namely discrete wavelet transform (DWT) as pre step before performing the agorthim. This transformation overcomes the low convergence rate of the second-order RI algorithm by reducing the selfcorrelation of the input signal in the mentioned scenatios. Expeirments are conducted using the noise cancellation setting. The performance of the proposed algorithm is compared to those of the RI, original second-order RI and RLS algorithms in different Gaussian and impulsive noise environments. Simulations demonstrate the superiority of the proposed algorithm in terms of convergence rate compared to those algorithms.

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Active noise cancellation frequency-locked loop with a notch filter

Pan

Zhang

et al. 2021

J. Power Electron.

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Stabilization of a Modified LMS Algorithm for Canceling Nonlinear Memory Effects

Cited by 7 publications

References 35 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

Discrete-wavelet-transform recursive inverse algorithm using second-order estimation of the autocorrelation matrix

Active noise cancellation frequency-locked loop with a notch filter

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