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In this paper we propose a new pipelined architecture for the DLMS algorithm which can be implemented with less than half an amount of calculation compared to the conventional architectures. Although the proposed architecture enables us to reduce the required calculation, it can achieve good convergence characteristics, a short latency and high throughput characteristics simultaneously.
This paper presents a novel adaptive algorithm for the estimation of discrete Fourier coefficients (DFC) of sinusoidal and/or quasi-periodic signals in additive noise. The algorithm is derived using a least mean p-power error criterion. It reduces to the conventional LMS algorithm when p takes on 2. It is revealed by both analytical results and extensive simulations that the new algorithm for p —...
The binormalized data-reusing least mean squares (BNDR-LMS) algorithm has been recently proposed and has been shown to have faster convergence than other LMS-like algorithms in cases where the input signal is strongly correlated. This superior performance in convergence speed is, however, followed by a higher misadjustment if the step-size is close to the value which allows the fastest convergence...
A new family of adaptive structures which employ filter banks or wavelets to decompose the input signal and reduced-order adaptive filters in the subbands is applied to the acoustic echo control problem. Structures with sparse adaptive subfiltcrs and no down-sampling of the subband signals, as well as structures with critical sampling of the subband signals, arc investigated. Both types of structures...
In this work we propose a novel scheme for adaptive system identification. This scheme is based on a normalized version of the least-mean fourth (LMF) algorithm. In contrast to the LMF algorithm, this new normalized version of the LMF algorithm is found to be independent of the input sequence autocorrelation matrix. It is also found that it converges faster than the normalized least mean square (NLMS)...
The good convergence tracking properties of spatio-temporal equalizers are pointed out and analyzed when there is effective diversity. The analysis is illustrated in the case of a frequency offset between the baud and sampling clocks that induces important time-variations.
The convergence speed of the filtered-x LMS algorithm is known to be slow due to the secondary acoustic path in front of the adaptive filter. Two methods are presented to improve the convergence properties of that algorithm. The exact method, referred to as the decor-related filtered-x algorithm, suffers from ‘the division by small number’ problem and an alternative approximate method is proposed...
This work presents an extension of the classical LMS-based Frost algorithm to include both the Normalized LMS (NLMS) and the Binormalized Data-Reusing LMS (BNDR-LMS) algorithms. Two simple versions of these algorithms are derived for the Frost structure. These new algorithms were applied to a DS-CDMA mobile receiver. The results showed a considerable speed up of the convergence rate compared to the...
This paper investigates the convergence properties of a variable step normalized LMS (VSNLMS) adaptive filter algorithm. Instead of a fixed step-size used in the conventional normalized LMS algorithm, the step-size of the algorithm under study is updated in each iteration, based on an expression related to the output errors. The variable step-size improves the convergence speed, while sacrificing...
Adaptive truncated Volterra filters using parallel-cascade structures are discussed in this paper. Parallel-cascade realizations implement higher-order Volterra systems as a parallel and multiplicative combination of lower-order Volterra systems. A normalized LMS adaptive filter for parallel-cascade structures is developed and its performance is evaluated through simulation experiments. The experimental...
The behaviour of the LMS adaptive algorithm is analyzed for a class of adaptive filters that is based on a cascade of identical N-th order all-pass sections. The well-known tapped-delay-line is a special case of this class. We look at the rate of convergence and the steady-state weight fluctuations. It is shown that in the steady state the weight-error correlation matrix satisfies a Lyapounov equation...
We propose two structures and theirs associated algorithms designed to solve the blind source separation problem in the presence of noise and interferences. Both structures exploit the non convexity of the Constant Modulus cost function, finding its multiple local minima. A convergence analysis shows that both schemes achieve the desired solution, separately extracting the sources of interest while...
Since many signal processing problems can be posed as sample-based decision and estimation tasks, we discuss how studies from other fields such as neural networks might suggest improved architectures (models) and algorithms for these types of problems. We then concentrate on PAM equalization, showing that a reordering of the equivalent classification problem generates a ‘staircase’ which, while retaining...
In this contribution a new robust technique for adjusting the step size of the Least Mean Squares (LMS) adaptive algorithm is introduced. The proposed method exhibits faster convergence, enhanced tracking ability and lower steady state excess error compared to the fixed step size LMS and other previously developed variable step size algorithms, while retaining much of the LMS computational simplicity...
An adaptive noise cancellation scheme for speech processing is proposed. In this, the adaptive filters are implemented in frequency-limited sub-bands, based on a simplified model of the human cochlea. A modification to the basic LMS structure is introduced which guarantees stability and convergence at all times. This modification, a non-recursive normalisation, is used both in a wideband and in a...
In this paper we outline a technique for increasing the convergence rate of the LMS algorithm by means of a preconditioning filter which reduces the eigenvalue spread of the input signal. Specifically we use a low order linear prediction lattice filter followed by a tapped-delay-line as the preconditioner. Some computer simulations are provided to demonstrate the increased convergence rate of the...
We propose a new variable step-size diffusion least mean square algorithm for distributed estimation that adaptively adjusts the step-size in every iteration. For a network application, the proposed method determines a suboptimal step-size at each node to minimize the mean square deviation for the intermediate estimate. The algorithm thus adapts the different node environments and profiles across...
Complex kernel-based adaptive algorithms have been recently introduced for complex-valued nonlinear system identification. These algorithms are built upon the same framework as complex linear adaptive filtering techniques and Wirtinger's calculus in complex reproducing kernel Hilbert spaces. In this paper, we study the convergence behavior of the augmented complex Gaussian KLMS algorithm. Simulation...
The Canonical Polyadic (CP) tensor decomposition has become an attractive mathematical tool these last ten years in various fields. Yet, efficient algorithms are still lacking to compute the full CP decomposition, whereas rank-one approximations are rather easy to compute. We propose a new deflation-based iterative algorithm allowing to compute the full CP decomposition, by resorting only to rank-one...
Although the LMS algorithm is often preferred in practice due to its numerous positive implementation properties, once the parameter space to estimate becomes large, the algorithm suffers of slow learning. Many ideas have been proposed to introduce some a-priori knowledge into the algorithm to speed up its learning rate. Recently also sparsity concepts have become of interest for such algorithms....
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