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Haiquan Zhao - One of the best experts on this subject based on the ideXlab platform.
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block sparse non uniform norm constraint normalised subband Adaptive Filter
Iet Signal Processing, 2019Co-Authors: Wenyuan Wang, Haiquan ZhaoAbstract:This study proposes a block-sparse non-uniform norm constraint normalised subband Adaptive Filter (BS-NNCNSAF) for the block-sparse system identification problem, which is obtained by minimising a novel cost function involving the non-uniform mixed l 2, p norm like a constraint. It can achieve better performance compared with the existing algorithms in the block-sparse system identification. To further enhance the performance of the algorithm, the shrinkage BS-NNCNSAF (SH-BS-NNCNSAF) algorithm is proposed. The proposed SH-BS-NNCNSAF algorithm is derived by taking the priori and the posteriori subband errors to achieve the time-varying subband step sizes. Finally, simulations have been carried out to verify the performance of proposed algorithms. The simulation results verify that the proposed algorithms improve the performance of the Filter, in terms of system identification in sparse systems.
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bias compensated constrained least mean square Adaptive Filter algorithm for noisy input and its performance analysis
Digital Signal Processing, 2019Co-Authors: Wenyuan Wang, Haiquan ZhaoAbstract:Abstract A bias-compensated constrained least mean square (BC-CLMS) Adaptive Filter algorithm for noisy input is proposed. To derive the proposed algorithm, we present a novel cost function whose gradient vector is unbiased. Thereby, the proposed algorithm can mitigate the effect of input noise and obtain an unbiased estimation. Then, the detail performance analysis of the proposed algorithm is also provided. Finally, simulations are carried out to illustrate the advantage of the proposed algorithm. In addition, the correctness of performance analysis is also verified by simulations.
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bias compensated Adaptive Filter algorithm under minimum error entropy criterion
IFAC-PapersOnLine, 2019Co-Authors: Zejun Chen, Haiquan ZhaoAbstract:Abstract This paper proposes a bias-compensated Adaptive Filtering algorithm under minimum error entropy criterion, which outperforms with low steady-state misalignment for signal processing with noisy input in an environment containing impulsive output noise. In previous studies, much works use the minimum error entropy criterion, which is called MEE, to develop Adaptive Filter under the assumption of no input noise. However, pure input signal without any noise is nonexistent in the real-world environment. To address above issue, we introduce a bias-compensated vector into the traditional MEE algorithm and propose a bias-compensated Adaptive Filtering algorithm under minimum error entropy criterion named BCMEE, which has stronger robustness and higher convergence rate. The BCMEE utilizes a kernel function. In the kernel function, the slicing window size takes certain numbers of past errors during adaptation processing in BCMEE’s updating, whereas the other classical algorithm relies only on the current error signal and takes advantage of bias-compensated vector to compensate the bias of Filter caused by the input noise. Simulation show the excellent performance of the proposed algorithms.
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l_ 1 norm constrained normalized subband Adaptive Filter algorithm with variable norm bound parameter and improved version
Signal Image and Video Processing, 2017Co-Authors: Long Shi, Haiquan ZhaoAbstract:The \(L_{1}\)-norm constrained normalized subband Adaptive Filter with variable norm-bound parameter \((L_{1}\hbox {NCNSAF-V})\) algorithm and its variable step size version VSS-\(L_{1}\)NCNSAF-V are proposed in this paper, which are more superior to some existing algorithms in the sparse system. The proposed \(L_{1}\)NCNSAF-V is derived by using the Lagrange multiplier method, and the VSS-\(L_{1}\)NCNSAF-V is obtained by minimizing the statistical square of the Euclidean norm of the noise-free subband a posterior error vector. The simulation results demonstrate that the proposed algorithms achieve good performance.
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sparse normalized subband Adaptive Filter algorithm with l0 norm constraint
Journal of The Franklin Institute-engineering and Applied Mathematics, 2016Co-Authors: Haiquan Zhao, Badong ChenAbstract:Abstract In order to improve the Filter׳s performance when identifying sparse system, this paper develops two sparse-aware algorithms by incorporating the l0-norm constraint of the weight vector into the conventional normalized subband Adaptive Filter (NSAF) algorithm. The first algorithm is obtained from the principle of the minimum perturbation; and the second one is based on the gradient descent principle. The resulting algorithms have almost the same convergence and steady-state performance while the latter saves computational complexity. What׳s more, the performance of both algorithms is analyzed by resorting to some assumptions commonly used in the analyses of Adaptive algorithms. Simulation results in the context of sparse system identification not only demonstrate the effectiveness of the proposed algorithms, but also verify the theoretical analyses.
Mario Huemer - One of the best experts on this subject based on the ideXlab platform.
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a robust nonlinear rls type Adaptive Filter for second order intermodulation distortion cancellation in fdd lte and 5g direct conversion transceivers
IEEE Transactions on Microwave Theory and Techniques, 2019Co-Authors: Andreas Gebhard, Oliver Lang, Michael Lunglmayr, Christian Motz, Ram Sunil Kanumalli, Christina Auer, Thomas Paireder, Matthias Wagner, Harald Pretl, Mario HuemerAbstract:Transceivers operating in frequency division duplex experience a transmitter leakage (TxL) signal into the receiver due to the limited duplexer stopband isolation. This TxL signal in combination with the second-order nonlinearity of the receive mixer may lead to a baseband (BB) second-order intermodulation distortion (IMD2) with twice the transmit signal bandwidth. In direct conversion receivers, this nonlinear IMD2 interference may cause a severe signal-to-interference-plus-noise ratio degradation of the wanted receive signal. This contribution presents a nonlinear Wiener model recursive-least-squares (RLS) type Adaptive Filter for the cancellation of the IMD2 interference in the digital BB. The included channel-select Filter and dc-notch Filter at the output of the proposed Adaptive Filter ensure that the provided IMD2 replica includes the receiver front-end Filtering. A second, robust version of the nonlinear recursive-least-squares (RLS) algorithm is derived which provides numerical stability for highly correlated input signals that arise in, e.g., Long-Term Evolution (LTE)-Advanced intra-band multi-cluster transmission scenarios. The performance of the proposed algorithms is evaluated by numerical simulations and by measurement data.
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a robust nonlinear rls type Adaptive Filter for second order intermodulation distortion cancellation in fdd lte and 5g direct conversion transceivers
arXiv: Signal Processing, 2018Co-Authors: Andreas Gebhard, Oliver Lang, Michael Lunglmayr, Christian Motz, Ram Sunil Kanumalli, Christina Auer, Thomas Paireder, Matthias Wagner, Harald Pretl, Mario HuemerAbstract:Transceivers operating in frequency division duplex experience a transmitter leakage (TxL) signal into the receiver due to the limited duplexer stop-band isolation. This TxL signal in combination with the second-order nonlinearity of the receive mixer may lead to a baseband (BB) second-order intermodulation distortion (IMD2) with twice the transmit signal bandwidth. In direct conversion receivers, this nonlinear IMD2 interference may cause a severe signal-to-interference-plus-noise ratio degradation of the wanted receive signal. This contribution presents a nonlinear Wiener model recursive least-squares (RLS) type Adaptive Filter for the cancellation of the IMD2 interference in the digital BB. The included channel-select-, and DC-notch Filter at the output of the proposed Adaptive Filter ensure that the provided IMD2 replica includes the receiver front-end Filtering. A second, robust version of the nonlinear RLS algorithm is derived which provides numerical stability for highly correlated input signals which arise in e.g. LTE-A intra-band multi-cluster transmission scenarios. The performance of the proposed algorithms is evaluated by numerical simulations and by measurement data.
Badong Chen - One of the best experts on this subject based on the ideXlab platform.
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sparse normalized subband Adaptive Filter algorithm with l0 norm constraint
Journal of The Franklin Institute-engineering and Applied Mathematics, 2016Co-Authors: Haiquan Zhao, Badong ChenAbstract:Abstract In order to improve the Filter׳s performance when identifying sparse system, this paper develops two sparse-aware algorithms by incorporating the l0-norm constraint of the weight vector into the conventional normalized subband Adaptive Filter (NSAF) algorithm. The first algorithm is obtained from the principle of the minimum perturbation; and the second one is based on the gradient descent principle. The resulting algorithms have almost the same convergence and steady-state performance while the latter saves computational complexity. What׳s more, the performance of both algorithms is analyzed by resorting to some assumptions commonly used in the analyses of Adaptive algorithms. Simulation results in the context of sparse system identification not only demonstrate the effectiveness of the proposed algorithms, but also verify the theoretical analyses.
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a new normalized subband Adaptive Filter algorithm with individual variable step sizes
Circuits Systems and Signal Processing, 2016Co-Authors: Haiquan Zhao, Badong ChenAbstract:Proposed is a novel variable step size normalized subband Adaptive Filter algorithm, which assigns an individual step size for each subband by minimizing the mean square of the noise-free a posterior subband error. Furthermore, a noniterative shrinkage method is used to recover the noise-free priori subband error from the noisy subband error signal. Simulation results using the colored input signals have demonstrated that the proposed algorithm not only has better tracking capability than the existing subband Adaptive Filter algorithms, but also exhibits lower steady-state error.
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steady state mean square deviation analysis of the sign subband Adaptive Filter algorithm
Signal Processing, 2016Co-Authors: Haiquan Zhao, Badong ChenAbstract:Recently, the sign subband Adaptive Filter (SSAF) algorithm has obtained great attention, due to its robustness against impulsive noises and decorrelating property for correlated input signals. However, the performance of the algorithm in the steady-state is not analyzed. In this paper, we study the steady-state mean-square-deviation (MSD) behavior of the SSAF algorithm by using energy conservation relation, Price's theorem and some reasonable assumptions. Simulation results in different system identification scenarios (including the input signals, tap lengths, impulsive noises, number of subbands, and step sizes) are provided to support our theoretical analysis.
Sang Woo Kim - One of the best experts on this subject based on the ideXlab platform.
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mean square deviation analysis of multiband structured subband Adaptive Filter algorithm
IEEE Transactions on Signal Processing, 2016Co-Authors: Jae Jin Jeong, Seung Hun Kim, Gyogwon Koo, Sang Woo KimAbstract:A multiband-structured subband Adaptive Filter (MSAF) algorithm was introduced to achieve a fast convergence rate for the correlated input signal. The convergence analysis of the Adaptive Filter algorithm is an important concept because it provides a guideline to design the Adaptive Filter. However, the convergence analysis of the MSAF algorithm has not been researched as extensively as that of the normalized least-mean-square algorithm. Therefore, it needs to be researched. In this paper, we present a new approach to the mean-square deviation (MSD) analysis of the MSAF algorithm by using the persistently exciting input and the practical assumption that the stopband attenuation of the prototype Filter is high. Unlike the previous analysis, the proposed analysis is possible to be applied to the long-length Adaptive Filter such as the acoustic echo cancellation. The proposed analysis is also applied to a non-stationary model with a random walk of the optimal weight vector. The simulation results match with the theoretical results in both the transient-state and steady-state MSD.
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steady state mean square deviation analysis of improved normalized subband Adaptive Filter
Signal Processing, 2015Co-Authors: Jae Jin Jeong, Gyogwon Koo, Keunhwi Koo, Sang Woo KimAbstract:A new minimization criterion for the normalized subband Adaptive Filter (NSAF), which is called improved NSAF (INSAF), was introduced recently to improve the performance of the steady-state mean-square deviation (MSD). However, the steady-state MSD analysis of the INSAF was not studied. Therefore, this paper proposes a general solution of steady-sate MSD analysis of the INSAF algorithm, which is based on the substitution of the past weight error vector in the weight error vector. The simulation shows that our theoretical results correspond closely with the computer simulation results in various environments.
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subband Adaptive Filter algorithm based on normalized least mean fourth criterion
International Conference on Signal Processing and Communication Systems, 2012Co-Authors: Jae Jin Jeong, Kyuhwan Kim, Sang Woo KimAbstract:A subband Adaptive Filter normalized least mean fourth criterion algorithm is proposed in which the fourth-order moments of subband error signals are minimized as cost functions. Simulation results verify that the proposed algorithm has a higher convergence rate than a conventional normalized subband Adaptive Filter algorithm in a non-Gaussian noise environment and for colored input signals.
Andreas Gebhard - One of the best experts on this subject based on the ideXlab platform.
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a robust nonlinear rls type Adaptive Filter for second order intermodulation distortion cancellation in fdd lte and 5g direct conversion transceivers
IEEE Transactions on Microwave Theory and Techniques, 2019Co-Authors: Andreas Gebhard, Oliver Lang, Michael Lunglmayr, Christian Motz, Ram Sunil Kanumalli, Christina Auer, Thomas Paireder, Matthias Wagner, Harald Pretl, Mario HuemerAbstract:Transceivers operating in frequency division duplex experience a transmitter leakage (TxL) signal into the receiver due to the limited duplexer stopband isolation. This TxL signal in combination with the second-order nonlinearity of the receive mixer may lead to a baseband (BB) second-order intermodulation distortion (IMD2) with twice the transmit signal bandwidth. In direct conversion receivers, this nonlinear IMD2 interference may cause a severe signal-to-interference-plus-noise ratio degradation of the wanted receive signal. This contribution presents a nonlinear Wiener model recursive-least-squares (RLS) type Adaptive Filter for the cancellation of the IMD2 interference in the digital BB. The included channel-select Filter and dc-notch Filter at the output of the proposed Adaptive Filter ensure that the provided IMD2 replica includes the receiver front-end Filtering. A second, robust version of the nonlinear recursive-least-squares (RLS) algorithm is derived which provides numerical stability for highly correlated input signals that arise in, e.g., Long-Term Evolution (LTE)-Advanced intra-band multi-cluster transmission scenarios. The performance of the proposed algorithms is evaluated by numerical simulations and by measurement data.
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a robust nonlinear rls type Adaptive Filter for second order intermodulation distortion cancellation in fdd lte and 5g direct conversion transceivers
arXiv: Signal Processing, 2018Co-Authors: Andreas Gebhard, Oliver Lang, Michael Lunglmayr, Christian Motz, Ram Sunil Kanumalli, Christina Auer, Thomas Paireder, Matthias Wagner, Harald Pretl, Mario HuemerAbstract:Transceivers operating in frequency division duplex experience a transmitter leakage (TxL) signal into the receiver due to the limited duplexer stop-band isolation. This TxL signal in combination with the second-order nonlinearity of the receive mixer may lead to a baseband (BB) second-order intermodulation distortion (IMD2) with twice the transmit signal bandwidth. In direct conversion receivers, this nonlinear IMD2 interference may cause a severe signal-to-interference-plus-noise ratio degradation of the wanted receive signal. This contribution presents a nonlinear Wiener model recursive least-squares (RLS) type Adaptive Filter for the cancellation of the IMD2 interference in the digital BB. The included channel-select-, and DC-notch Filter at the output of the proposed Adaptive Filter ensure that the provided IMD2 replica includes the receiver front-end Filtering. A second, robust version of the nonlinear RLS algorithm is derived which provides numerical stability for highly correlated input signals which arise in e.g. LTE-A intra-band multi-cluster transmission scenarios. The performance of the proposed algorithms is evaluated by numerical simulations and by measurement data.
