scholarly journals Global Estimation and Compensation of Linear Effects in Coherent Optical Systems based on Nonlinear Least Squares

2021 ◽  
Author(s):  
Alexandru Frunza ◽  
Vincent Choqueuse ◽  
Pascal Morel ◽  
Stéphane Azou
Keyword(s):  
Least Squares ◽  
Carrier Frequency Offset ◽  
Chromatic Dispersion ◽  
Nonlinear Least Squares ◽  
Digital Signal ◽  
Optical Systems ◽  
Computational Time ◽  
Iq Imbalance ◽  
Linear Effects ◽  
Compensation Approach

This paper proposes a new estimation and compensation approach to mitigate several linear and widely linear effects in coherent optical systems using digital signal processing (DSP) algorithms. Compared to most of the available strategies that employ local estimation and/or compensation algorithms, this approach performs a global impairments estimation and compensation based on Nonlinear Least Squares. The proposed method estimates and compensates for the chromatic dispersion (CD), carrier frequency offset (CFO), in-phase/quadrature (IQ) imbalance, and laser phase noise (PN) in two steps. Firstly, it estimates the quasi-static parameters related to the CD, CFO, and both transmitter and receiver IQ imbalance. Secondly, it estimates both transmitter and receiver lasers’ phases and compensates for all the imperfections by using a Zero-Forcing (ZF) equalizer. Simulations show the effectiveness of the approach in terms of statistical performance and computational time. The estimation performance is assessed by computing the Cramér Rao Lower Bound (CRLB), while the detection performance is compared to a modified Clairvoyant equalizer.<br>

scholarly journals Global Estimation and Compensation of Linear Effects in Coherent Optical Systems based on Nonlinear Least Squares

2021 ◽  
Author(s):  
Alexandru Frunza ◽  
Vincent Choqueuse ◽  
Pascal Morel ◽  
Stéphane Azou
Keyword(s):  
Least Squares ◽  
Carrier Frequency Offset ◽  
Chromatic Dispersion ◽  
Nonlinear Least Squares ◽  
Digital Signal ◽  
Optical Systems ◽  
Computational Time ◽  
Iq Imbalance ◽  
Linear Effects ◽  
Compensation Approach

This paper proposes a new estimation and compensation approach to mitigate several linear and widely linear effects in coherent optical systems using digital signal processing (DSP) algorithms. Compared to most of the available strategies that employ local estimation and/or compensation algorithms, this approach performs a global impairments estimation and compensation based on Nonlinear Least Squares. The proposed method estimates and compensates for the chromatic dispersion (CD), carrier frequency offset (CFO), in-phase/quadrature (IQ) imbalance, and laser phase noise (PN) in two steps. Firstly, it estimates the quasi-static parameters related to the CD, CFO, and both transmitter and receiver IQ imbalance. Secondly, it estimates both transmitter and receiver lasers’ phases and compensates for all the imperfections by using a Zero-Forcing (ZF) equalizer. Simulations show the effectiveness of the approach in terms of statistical performance and computational time. The estimation performance is assessed by computing the Cramér Rao Lower Bound (CRLB), while the detection performance is compared to a modified Clairvoyant equalizer.<br>

scholarly journals Separable Nonlinear Least-Squares Parameter Estimation for Complex Dynamic Systems

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Itai Dattner ◽  
Harold Ship ◽  
Eberhard O. Voit
Keyword(s):  
Least Squares ◽  
Dynamic Models ◽  
Least Squares Method ◽  
Substantial Reduction ◽  
Nonlinear Least Squares ◽  
Computational Time ◽  
Complex Dynamic ◽  
Nonlinear Dynamic Models ◽  
Nonlinear Least Squares Method ◽  
Parameter Values

Nonlinear dynamic models are widely used for characterizing processes that govern complex biological pathway systems. Over the past decade, validation and further development of these models became possible due to data collected via high-throughput experiments using methods from molecular biology. While these data are very beneficial, they are typically incomplete and noisy, which renders the inference of parameter values for complex dynamic models challenging. Fortunately, many biological systems have embedded linear mathematical features, which may be exploited, thereby improving fits and leading to better convergence of optimization algorithms. In this paper, we explore options of inference for dynamic models using a novel method of separable nonlinear least-squares optimization and compare its performance to the traditional nonlinear least-squares method. The numerical results from extensive simulations suggest that the proposed approach is at least as accurate as the traditional nonlinear least-squares, but usually superior, while also enjoying a substantial reduction in computational time.

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