تجزیه و تحلیل عملکرد از الگوریتم فیلترینگ تطبیقی DCT-LMS
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|27536||2000||26 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Signal Processing, Volume 80, Issue 8, August 2000, Pages 1629–1654
This paper presents the convergence analysis result of the discrete cosine transform-least-mean-square (DCT-LMS) adaptive filtering algorithm which is based on a well-known interpretation of the variable stepsize algorithm. The time-varying stepsize of the DCT-LMS algorithm is implemented by the modified power estimator to redistribute the spread power after the DCT. The performance analysis is considerably simplified by the modification of a power estimator. First of all, the proposed DCT-LMS algorithm has a fast convergence rate when compared to the LMS, the normalised LMS (NLMS), the variable stepsize LMS (VSLMS) algorithm for a highly correlated input signal, whilst constraining the level of the misadjustment required by a specification. The main contribution of this paper is the statistical performance analysis in terms of the mean and mean-squared error of the weight error vector. In addition, the decorrelation property of the DCT-LMS is derived from the lower and upper bounds of the eigenvalue spread ratio, λmax/λmin. It is also shown that the shape of sidelobes affecting the decorrelation of the input signal is governed by the location of two zeros. Theoretical analysis results are validated by the Monte Carlo simulation. The proposed algorithm is also applied in the system identification and the inverse modelling for a channel equalisation in order to verify its applicability.
Adaptive filtering algorithms based on the stochastic gradient method are widely used in many applications such as system identification, noise cancellation, active noise control and communication channel equalisation. The least mean square (LMS) which belongs to the stochastic gradient-type algorithm has been the focus of much study due to its simplicity and robustness. However, it is well known that the convergence rate is seriously affected by the correlation of an input signal. To circumvent this inherent limitation, many algorithms have been implemented. As one of popular approaches, the transform domain least-mean-square (TDLMS) adaptive filtering algorithms , , , ,  and  have been developed to improve a slow convergence rate caused by an ill-conditioned input signal. In 1983, Narayan  first introduced the TDLMS algorithm which uses the orthogonal transform matrices of the discrete Fourier transform (DFT) and the discrete cosine transform (DCT). The enhanced convergence rate when compared with the conventional LMS algorithm was verified empirically. However, focus was not placed on theoretical analysis. The performance was judged purely by computer simulation. In 1988, Florian  analysed the performance of the weighted normalised LMS algorithm via exponential weighted parameters. It was analysed only for the mean behaviour of weights. However, a general derivation was not obtained. In 1989, Marshall  investigated the convergence property through the computer simulation for several unitary transform matrices. In his work, transform domain processing was characterised by the effect of the transform on the shape of the error performance surface. In 1995, Beaufay  also studied analytically the behaviour of the eigenvalue spread for a first-order Markov process in the discrete Fourier transform least-mean-square (DFT-LMS) and the discrete cosine transform least-mean-square (DCT-LMS) algorithms. In most recent work (1997) , Parikh proposed the modified escalator structure to improve the performance of the LMS adaptive filter. The algorithm utilised the sparse structure of the correlation matrix. The sparse structure is extracted from the unitary transform matrix of the DCT to be applied in the escalator structure of the lattice model. This filter is not an efficient filtering structure in that it employs two transform layers: a unitary transform matrix and an escalator structure of the lattice model. The first transform layer by a unitary transform matrix does not affect the convergence speed because a correlated input signal is decorrelated by the escalator structure of the second layer. As another alternative technique to overcome a slow convergence rate, the variable step-size LMS (VSLMS) algorithms , ,  and  have been developed to enhance the convergence rate and to reduce the misadjustment error in the state space. However, they might not be also effective for a highly correlated input signal. This is because the dynamic range of the variable stepsize is restricted by a directional convergence nature. In addition, the normalised LMS (NLMS) might be efficient and robust algorithm for the nonstationary input process, however, it also suffers from a slow convergence speed if driven by a highly correlated input signal. To resolve this problem, Ozeki  and Rupp  proposed the so-called affine projection algorithms to decorrelate an input signal. In the first part of this paper, we analyse the decorrelation properties by the measure of the eigenvalue spread ratio, the complementary spectrum principle and the pole-zero location. It has been known that the DCT decorrelates effectively the input signal whose power spectrum lies in the low-frequency band. Boroujeny  explained intuitively the decorrelation feature of the DCT for the lowpass input process from the filtering viewpoint. However, this work did not show analytically how the DCT can decorrelate the input signals with the low-frequency input spectrum, similarly to the Karhunen–Loève transform (KLT). In the second part of this paper, we analyse the convergence behaviour of the DCT-LMS adaptive filtering algorithm which is based on a well-known interpretation of the variable stepsize algorithm. A time-varying stepsize is implemented by the modified power estimator to redistribute the spread power after the transformation. This modification makes the performance analysis simple. As we have investigated the previous work relevant to the transform domain adaptive filtering structure , , ,  and , so far, only a limited analysis of the TDLMS algorithm has been performed due to the difficulty of the analytical derivation for the normalisation term. The exponential weighted method is generally used for obtaining the convergence parameter μi(n) which is equation(1) at the ith bin of transform domain, where β∈[0,1], is the power estimator, μi denotes elements of the diagonal matrix defined as and xi(n) is the transformed input signal at the ith bin. The exponential weighted parameter also has the recursive form equation(2) In this paper, we propose the modified power estimator based upon (1) equation(3) where β∈[0,1],γ∈[0,1],0<ε≪1,i=0,…,N−1, and M denotes the size of sample to estimate the power at the ith bin after transformation. The main contribution of this paper is the statistical performance analysis of the DCT-LMS adaptive filtering algorithm based on the modified power estimator. In addition, the decorrelation properties of the DCT is described from the lower and upper bounds of the eigenvalue spread ratio. In particular, it is shown that the shape of sidelobes affecting the decorrelation of the input signal is governed by the location of two zeros. The theoretical analysis results are validated by the Monte Carlo simulation. The rest of this paper is organised as follows. In Section 2, the decorrelation properties are investigated. In Section 3, the DCT-LMS adaptive filter via the modified power estimator is described. In Section 4, the convergence behaviour of the proposed algorithm is analysed. In Section 5, the computer simulation is undertaken in the system identification and the channel equalisation examples to verify the performance of the proposed DCT-LMS algorithm. The simulation results are compared to the standard LMS, the NLMS and the VSLMS algorithms . Conclusions are then given in Section 6.
نتیجه گیری انگلیسی
The DCT-LMS algorithm employing a new type of power estimator has been introduced. It was shown that the modified power estimator for the DCT-LMS algorithm works properly as a time-varying stepsize to redistribute the spread power after the DCT transformation. In particular, the decorrelation properties of the unitary transform matrix have been investigated theoretically. It was found that the decorrelation properties of the DCT is governed by the location of two zeros and its property was derived from the lower and upper bounds of the eigenvalue spread ratio. The performance analysis of the DCT-LMS algorithm with a variable stepsize has been derived in terms of the mean and mean-squared error. Monte Carlo simulation was found to give a good fit to our theoretical predictions. However, it was shown that a large stepsize by the control parameter γ violates Assumption 3 as expected. The proposed filtering algorithm has a convergence rate that is greater than that of the plain LMS, the NLMS, the VSLMS algorithm at the expense of more NM−N (multiplications) and M (additions) than the conventional transform domain algorithm.