Block LMS Algorithm Research Articles

Lattice PFBLMS: Fast converging structure for efficient implementation of frequency-domain adaptive filters

It has been demonstrated that the unconstrained partitioned fast block LMS (PFBLMS) algorithm suffers from an inherently slow convergence rate. A solution to this problem has previously been proposed by the second author of this paper, which reduces the correlation by reducing the overlap between partitions. A drawback of this solution is that it requires more memory than the conventional PFBLMS algorithm. In this paper, we propose an alternative solution that does not require any additional memory. A lattice structure is used instead to remove the correlation and increase the convergence rate.

Systolic array implementation of block LMS algorithm

The authors derive the systolic array implementation of the block LMS algorithm, consisting of N processing elements, where N is the filter order. The resulting array attains an order-independent sampling rate. Computer simulation results show that the block LMS algorithm is faster than the delayed LMS algorithm. which has previously been implemented on systolic arrays.

A block LMS algorithm for third-order frequency-domain Volterra filters

The objective of this paper is to present an adaptive algorithm to identify third-order frequency-domain Volterra filter coefficients, which correspond to the discrete Fourier transform (DFT) of the time-domain Volterra filter coefficients. The approach rests upon the block least mean square (LMS) algorithm based on the overlap-save method.

Block momentum-LMS algorithm based on the method of parallel tangents

Based on the method of parallel tangents, the block-LMS algorithm is modified, and the block momentum-LMS algorithm is proposed. The new algorithm has lower computational complexity than the LMS algorithm. It converges significantly faster than the block-LMS algorithm when the input signal is coloured. The time-constant, mean and mean-square convergence conditions and the misadjustment of the proposed algorithm are derived. As a special case, an accurate mean-square convergence condition is obtained for the block-LMS algorithm. Extension to the frequency domain is also discussed. Comprehensive experimental results on system identification and channel equalisation are presented that validate the theoretical findings.

The comparison of the constrained and unconstrained frequency-domain block-LMS adaptive algorithms

Convergence properties of the constrained and the unconstrained frequency-domain block LMS algorithms are analyzed. Comparisons based on both a theoretical analysis and computer simulation are given. It is shown that the unconstrained algorithm has a slower convergence rate and smaller stable range of step size than that of the constrained algorithm.

Analysis and efficient implementation of partitioned block LMS adaptive filters

The frequency domain block LMS (FBLMS) algorithm used for efficient implementation of adaptive filters whose length may exceed a few thousands of taps is explored further by Asharif et al. (1980). To reduce the latency of such filters, it has been proposed that the adaptive filter convolution sum be partitioned into a few smaller sums and then the FBLMS be applied. We show that the scheme proposed by Asharif et al. suffers from a serious eigenvalue spread problem. We identify the source of this problem and propose a solution to that.

Nonlinear quantization effects in the LMS and block LMS adaptive algorithms-a comparison

Digital implementations of the least-mean-square (LMS) and block LMS (BLMS) algorithms are compared with respect to finite word effects. The algorithm stalling phenomenon is studied using Gaussian data models and conditional expectation arguments. It is shown that the BLMS algorithm requires (1/2 log/sub 2/ L-K) fewer bits for the same stalling behavior (L=block length and K lies between 0.2 and 1.0, depending on the precise definition of algorithm stalling). On the other hand, the LMS algorithm requires log/sub 2/ L fewer bits than BLMS for the same level of saturation behavior (transient response) at algorithm initialization. Hence, the LMS algorithm requires (1/2 log/sub 2/ L+K) fewer bits than the BLMS algorithm for the same saturation and stalling effects. >

Performance of a class of adaptive data-driven echo cancellers

A study is made of the performance of a class of adaptive data-driven echo cancellers (DDECs). The authors first investigate the interrelationship in a unified framework among the system equations of various data-driven echo cancellers. As a result, they obtain a new DDEC algorithm that consists of only one real-valued adaptive structure. The authors analyze and compare the convergence behavior of DDECs. They analyze their complexities when the least-mean-square (LMS) algorithm and the frequency-domain block LMS algorithm are used for adjusting the canceller weights. The results show that the echo canceller structure realized in the frequency domain has advantages in convergence rate and in implementation complexity as compared to existing DDEC structures. >

A reduced structure of the passband fractionally spaced equalizer

In this letter, we propose a reduced structure for realization of a passband fractionally spaced equalizer (FSE) using the block LMS (BLMS) algorithm. Since this structure consists of one real transversal filter, its complexity is considerably simpler than existing equalizer structures. The passband FSE using the real passband input converges to the same steady-state value as that of the FSE with the complex analytic input, but its convergence time takes twice as long.

A frequency-weighted block LMS algorithm and its application to speech processing

In this letter, we propose a frequency-weighted block LMS (FWBLMS) algorithm that is based on minimization of the frequency-weighted block mean-squared error. The FWBLMS algorithm has an important advantage over the time-domain LMS algorithm in that it can be designed with different frequency weighting on error signal depending on the relative significance of various frequency bands. Application of the FWBLMS algorithm in adaptive linear prediction of speech is discussed.