Improving the Performance of the Digital Predistorter Based on Sample Reuse- Rls Algorithm

Alngar, Omar Z.; El-Deeb, Walid S.; El-Rabaie, El-Sayed M.

doi:10.1109/jec-ecc.2018.8679560

Cited by 2 publications

(9 citation statements)

References 11 publications

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“…Should the value of the error at an instantaneous time sample be above the error threshold at an particular time sample, the algorithm re-computes the error for the following time sample, omitting the previous from the calculation of the DPD coefficients. The methodology proposed by [8] reports a 30dB improvement when compared to the un-modified RLS algorithm. The validation performed in [8], using a memory depth of 2, is simulated but the methodology would be robust against spurious noise.…”

Section: Related Workmentioning

confidence: 99%

“…The methodology proposed by [8] reports a 30dB improvement when compared to the un-modified RLS algorithm. The validation performed in [8], using a memory depth of 2, is simulated but the methodology would be robust against spurious noise. Authors of [8] present results which indicate severe latency.…”

Section: Related Workmentioning

confidence: 99%

“…Recent research [8] presents a method to modify the RLS algorithm by applying an error threshold. The update is based on the computation of the error as seen in (8).…”

Section: Related Workmentioning

confidence: 99%

“…Recent research [8] presents a method to modify the RLS algorithm by applying an error threshold. The update is based on the computation of the error as seen in (8). Should the value of the error at an instantaneous time sample be above the error threshold at an particular time sample, the algorithm re-computes the error for the following time sample, omitting the previous from the calculation of the DPD coefficients.…”

Section: Related Workmentioning

confidence: 99%

“…The validation performed in [8], using a memory depth of 2, is simulated but the methodology would be robust against spurious noise. Authors of [8] present results which indicate severe latency.…”

Section: Related Workmentioning

confidence: 99%

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Early Stopping Criterion for Recursive Least Squares Training of Behavioural Models

Loughman

Barton

Farrell

et al. 2022

Wireless Pers Commun

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The necessity of the rapid evolution of wireless communications, with continuously increasing demands for higher data rates and capacity Zheng (Big datadriven optimization for mobile networks toward 5g 30:44–51, 2016), is constantly augmenting the complexity of radio frequency (RF) transceiver architecture. A significant component in the configuration of such complex radio transceivers is the power amplifier(PA). Multiple distributed PAs are now common in proposed RF architectures. PAs exhibit non linear behaviour, causing signal distortion in transmission. Behavioural models offer a concise representation of a PAs characteristic performance which is extremely useful in simulating performance of multiple nonlinear power amplifiers. A considerable drawback with using the Recursive Least Squares (RLS) technique is that the instability of the coefficients during the training of the model. This manuscript provides a computationally efficient technique to detect the onset of instability during adaptive RLS training and subsequently to inform the decision to cease training of dynamic memory polynomial based behavioural models, to avoid the onset of instability. The proposed technique does not require modification of the RLS algorithm, merely an observation of the pre-exsisting autocorrelation function based update. This technique is experimentally validated using four different signal modulation schemes, LTE OFDM, 5G-NR, DVBS2X and WCDMA.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…Recent research [8] presents a method to modify the RLS algorithm by applying an error threshold. The update is based on the computation of the error as seen in (8).…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 3 more Smart Citations