New Nonlinear Signal Processing Method Based on LS Algorithm and Decision Feedback at Receiver

Xi, Zhaoyang; Chen, Jinshu

doi:10.1109/ictc49638.2020.9123285

Cited by 3 publications

(2 citation statements)

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“…Since (1) is linear with respect to its coefficients, using the least squares (LS) algorithm 14 can obtain the coefficients. Letting

{u}_{1, kq}=x\left(n-q\right){\left|x\left(n-q\right)\right|}^{k-1}

{u}_{2, kq}=x(n){\left|x\left(n-q\right)\right|}^{k-1}

{u}_{3, kq}=x(n){\left|x\left(n-q\right)\right|}^{k-1}

\mathbf{y}

can be obtained as

\mathbf{y}=\mathbf{Up},

where

\mathbf{y}={\left[y(0),\cdots, y\left(N-1\right)\right]}^T

\mathbf{p}

is a vector of the nonlinear model coefficients, and

\mathbf{U}

is a matrix of all

{u}_{1, kq}

{u}_{2, kq}

…”

Section: Methods Of Modeling Nonlinear Behavior Of Transmittermentioning

confidence: 99%

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A Digitally assisted nonlinearity suppression architecture using over‐the‐air beam cancelation

Chen

et al. 2023

Int J Numerical Modelling

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Using a large number of power amplifiers brings serious nonlinear distortion to 5G communication systems, which becomes a challenging problem for conventional digital predistortion (DPD) to linearize signals. To address this challenge, we propose a digitally assisted nonlinearity suppression architecture for applications in multiple antenna arrays. In this architecture, we use an auxiliary array to generate a nonlinear cancelation signal to cancel with the main beam at the receiving end. We explore a two‐step method to model the nonlinear signal and combine neural network to complete the modeling. In addition, we introduce a novel method for estimating and tracking the channel to update the model coefficients. To verify the proposed methods, we design a scene and compare the performance of our architecture with that of conventional DPD. Our experiments demonstrate that our proposed methods can significantly enhance the adjacent channel power leakage ratio performance by 12 dB while also optimizing in‐band distortion. Moreover, our architecture outperforms conventional DPD approaches even with enhanced distortion.

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“…Since (1) is linear with respect to its coefficients, using the least squares (LS) algorithm 14 can obtain the coefficients. Letting

{u}_{1, kq}=x\left(n-q\right){\left|x\left(n-q\right)\right|}^{k-1}

{u}_{2, kq}=x(n){\left|x\left(n-q\right)\right|}^{k-1}

{u}_{3, kq}=x(n){\left|x\left(n-q\right)\right|}^{k-1}

\mathbf{y}

can be obtained as

\mathbf{y}=\mathbf{Up},

where

\mathbf{y}={\left[y(0),\cdots, y\left(N-1\right)\right]}^T

\mathbf{p}

is a vector of the nonlinear model coefficients, and

\mathbf{U}

is a matrix of all

{u}_{1, kq}

{u}_{2, kq}

…”

Section: Methods Of Modeling Nonlinear Behavior Of Transmittermentioning

confidence: 99%

“…Since ( 1) is linear with respect to its coefficients, using the least squares (LS) algorithm 14 can obtain the coefficients. Letting u 1,kq ¼ x nÀ q ð Þjx nÀ q ð Þj kÀ1 , u 2,kq ¼ x n ð Þjx nÀ q ð Þj kÀ1 , u 3,kq ¼ x n ð Þjx nÀ q ð Þj kÀ1 , y can be obtained as…”

Section: Nonlinear Canceling Signal Reconstruction Based On Gmpmentioning

confidence: 99%