Adaptive Subspace Method for Phase Noise Estimation in CO-OFDM Systems

Balogun, Muyiwa B.; Oyerinde, Olutayo O.; Takawira, Fambirai

doi:10.1109/access.2018.2868718

Cited by 6 publications

(8 citation statements)

References 36 publications

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“…. , g g g [1]] H , and λ ∈ (0, 1] is a positive forgetting factor (FF). For the simple IV method, the inverse of C C C G [n] can be recursively updated from (15a) using the matrix inversion lemma.…”

Section: B Recursive Estimation Of a A A And C C Cmentioning

confidence: 99%

“…is the derivative of h h h k (t) at time t n and r r r k (t m − t n ) is the remainder. Both h h h (1) k (t n ) and r r r k (t m − t n ) can be considered as random vectors and the latter is of order…”

Section: The Proposed Sc-vff-sreiv and -Rls Rsi Algorithm A Thementioning

confidence: 99%

“…For simplicity, we also assume that h h h (1) k (t n ) and r r r k (t m − t n ) are stationary processes inside B(t n ).…”

Section: The Proposed Sc-vff-sreiv and -Rls Rsi Algorithm A Thementioning

confidence: 99%

“…k [n] can be estimated by the difference of the weight vector obtained at time instances n and n − 1 asĥ h h (1)…”

Section: ) Vff Recursive Eiv Algorithm For Estimating a A A And C C Cmentioning

confidence: 99%

“…Subspace-based approaches play an important role in system identification [1]- [4] due to its theoretical and practical convenience for building state-space models directly from the input-output data of systems. Offline subspace identification techniques are characterized by the use of robust numerical tools such as the RQ factorization and the singular The associate editor coordinating the review of this manuscript and approving it for publication was Anand Paul .…”

Section: Introductionmentioning

confidence: 99%

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A Variable Regularized Recursive Subspace Model Identification Algorithm With Extended Instrumental Variable and Variable Forgetting Factor

2020

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This paper proposes a new smoothly clipped absolute deviation (SCAD) regularized recursive subspace model identification algorithm with square root(SR) extended instrumental variable (EIV) and locally optimal variable forgetting factor (LOFF). The proposed algorithm is based on a new local polynomial modeling (LPM)-based variable FF EIV projection approximation subspace tracking (PAST) and multivariate recursive least squares algorithms for estimating respectively the subspace of the extended observability matrix, and the input matrix and feedthrough matrix. The SREIV-PAST algorithm offers improved resilience to additive noise over the PAST algorithm and it is implemented using the more stable square-root form. The asymptotic mean square error of the corresponding LPM-based model is derived and minimized at each time instant to obtain the proposed LOFF for improving the convergence speed and steady state error. A recursive bi-iteration singular value decomposition (SVD) algorithm is also proposed for recursive computation of the pseudo-inverse of the state transition matrix and its eigenvalues. This facilitates online estimation of its model order using classical model selection criteria. Moreover, a new criterion based on the percentage of explained variance is also proposed with improved performance. The SCAD regularization is proposed for automatic model selection of the input and feedthrough matrices, as it is asymptotically unbiased. Efficient techniques for incorporating SCAD and estimating other required quantities are also developed. The proposed algorithms are evaluated using computer simulations under stationary and nonstationary environments and a real dataset on wing flutter data. Results show that the proposed algorithms offer improved convergence speed and steady state performance over the conventional algorithms and it also provides an online estimate of the system model order. INDEX TERMS Recursive subspace model identification (RSI), variable forgetting factor (VFF), SCAD regularization, extended instrumental variable (EIV), local polynomial modeling (LPM), model order estimation.

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Section: B Recursive Estimation Of a A A And C C Cmentioning

confidence: 99%

Section: The Proposed Sc-vff-sreiv and -Rls Rsi Algorithm A Thementioning

confidence: 99%

“…For simplicity, we also assume that h h h (1) k (t n ) and r r r k (t m − t n ) are stationary processes inside B(t n ).…”

Section: The Proposed Sc-vff-sreiv and -Rls Rsi Algorithm A Thementioning

confidence: 99%

“…k [n] can be estimated by the difference of the weight vector obtained at time instances n and n − 1 asĥ h h (1)…”

Section: ) Vff Recursive Eiv Algorithm For Estimating a A A And C C Cmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Variable Regularized Recursive Subspace Model Identification Algorithm With Extended Instrumental Variable and Variable Forgetting Factor

2020

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CO-OFDM System with Self-Organizing Map (SOM) for Improving the Spectral Efficiency by the Compensation of Phase Noise

Kaur,

Kaur

2024

Lecture Notes in Electrical Engineering

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WRELM based CO-OFDM system with improved performance, spectral efficiency and computational complexity

Kaur

2021

Opt Quant Electron

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Adaptive Subspace Method for Phase Noise Estimation in CO-OFDM Systems

Cited by 6 publications

References 36 publications

A Variable Regularized Recursive Subspace Model Identification Algorithm With Extended Instrumental Variable and Variable Forgetting Factor

A Variable Regularized Recursive Subspace Model Identification Algorithm With Extended Instrumental Variable and Variable Forgetting Factor

CO-OFDM System with Self-Organizing Map (SOM) for Improving the Spectral Efficiency by the Compensation of Phase Noise

WRELM based CO-OFDM system with improved performance, spectral efficiency and computational complexity

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