The bin-normalized frequency-domain block LMS (FBLMS) algorithm has low computational burden and potential fast convergence;however, it suffers from a biased steady-state solution when the reference signal lags behind...
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The bin-normalized frequency-domain block LMS (FBLMS) algorithm has low computational burden and potential fast convergence;however, it suffers from a biased steady-state solution when the reference signal lags behind the desired signal or the adaptive filter is of insufficient length. This paper proposes a unified framework for the FBLMS algorithm, which can be used to comprehensively analyze its steady-state behavior. Furthermore, a modified FBLMS algorithm with guaranteed optimal steady-state performance is proposed based on the framework. Simulations are carried out to demonstrate the benefit of the proposed algorithm. (C) 2014 Elsevier B.V. All rights reserved.
A frequency-domain implementation of the soft constraint satisfaction multimodulus blind algorithm is proposed. This leads to a significant reduction in the computational complexity, thus making it suitable for broadb...
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A frequency-domain implementation of the soft constraint satisfaction multimodulus blind algorithm is proposed. This leads to a significant reduction in the computational complexity, thus making it suitable for broadband wireless systems. The convergence of the developed algorithm is then improved by normalising each of the frequency bins in the weight update. Simulation results support the superior performance of the proposed algorithm over its time domain counterpart in terms of faster convergence rate.
作者：
Lee, JCAjou Univ
Dept Syst Engn Yongin 449820 Kyonggido South Korea Inst Adv Engn
Yongin 449820 Kyonggido South Korea
The implementation of forward/backward least mean square linear predictors in the frequency-domain is known to require two DFT/FFT operations. Were, computationally efficient structures are derived using proper data a...
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The implementation of forward/backward least mean square linear predictors in the frequency-domain is known to require two DFT/FFT operations. Were, computationally efficient structures are derived using proper data augmentation methods. As a result, the Following are achieved: (i) reduction of a DFT/FFT operation: and (ii) further reduction ill complexity when the input signal is real-valued.
Normalizing the convergence coefficient of the block frequency-domain least mean square (LMS) algorithm in each frequency bin can improve the convergence rate, but in some applications can lead to a biased steady-stat...
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Normalizing the convergence coefficient of the block frequency-domain least mean square (LMS) algorithm in each frequency bin can improve the convergence rate, but in some applications can lead to a biased steady-state solution if the filter is constrained to be strictly causal. An algorithm is presented in which the spectral factors of the bin-normalized convergence coefficient are used before and after the causality constraint is applied in the adaptation algorithm, which converges rapidly to the optimal causal filter.
Although the bin-normalized frequencydomain block LMS (NFBLMS) algorithm has theoretically very fast convergence speed, it suffers from convergence to a biased steady state solution when causality is not met or when ...
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Although the bin-normalized frequencydomain block LMS (NFBLMS) algorithm has theoretically very fast convergence speed, it suffers from convergence to a biased steady state solution when causality is not met or when the adaptive filter is of deficient length. A modified FBLMS (MFBLMS) algorithm with guaranteed optimal steady state performance has been proposed, but the theoretical analysis on its convergence properties has not been presented. This paper analyzes the convergence behavior of the algorithm by using the theory of asymptotically equivalent matrices. The eigenvalues of the matrix controlling the convergence behavior is proven to have the tendency to be equally distributed, and a theoretical eigenvalue spread is derived based on the first-order autoregressive (AR) signal model, which is significantly lower than that of the time domain LMS (TDLMS) algorithm. Therefore the convergence speed of the MFBLMS is significantly higher than that of the TDLMS algorithm for colored reference signal. Simulations are carried out to validate the convergence behavior predicted from the theoretical analysis.
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