Shin'ichi Koike

Analysis of adaptive filters using normalized signed regressor LMS algorithm

In this paper, adaptive filters using the normalized signed regressor LMS algorithm (NSRA) with Gaussian reference inputs are proposed and analyzed to yield difference equations for theoretically calculating expected convergence of the filters. A simple difference equation for mean squared error (MSE) is derived when the filter input is a white and Gaussian process, whereas approximate difference equations for colored Gaussian inputs are proposed and tested. Stability conditions and residual MSE after convergence are also obtained. Agreement of theoretical results with those of simulation in the experiment with some examples of filter convergence shows sufficient accuracy of the theory and assures the usefulness of the difference equations in estimating filter performances, thus facilitating the design of adaptive filters using the NSRA.

A class of adaptive step-size control algorithms for adaptive filters

A class of new adaptive step-size control algorithms, which is applicable to most of the LMS-derived tap weight adaptation algorithms, is proposed. Analysis yields a set of difference equations for theoretically calculating the transient behavior of the filter convergence and derives an explicit formula for the steady-state excess mean-square error (EMSE). Experiments for some examples prove that the proposed algorithm is highly effective in improving the convergence rate in both transient and tracking phases. The theoretically calculated convergence is shown to be in good agreement with that obtained through simulations. Alternative formulae of the step-size adaptation for specific tap weight adaptation algorithms are also proposed.

Analysis of the sign-sign algorithm based on Gaussian distributed tap weights

In this paper, a new set of difference equations is derived for convergence analysis of adaptive filters using the sign-sign algorithm with Gaussian input reference and additive Gaussian noise. The analysis is based on the assumption that the tap weights are jointly Gaussian distributed. Residual mean squared error after convergence and simpler approximate difference equations are further developed. Results of experiment exhibit good agreement between theoretically calculated convergence and that of simulation for a wide range of parameter values of adaptive filters.

Convergence Analysis of Adaptive Filters Using Normalized Sign-Sign Algorithm

This letter develops convergence analysis of normalized sign-sign algorithm (NSSA) for FIR-type adaptive filters, based on an assumption that filter tap weights are Gaussian distributed. We derive a set of difference equations for theoretically calculating transient behavior of filter convergence, when the filter input is a White & Gaussian process. For a colored Gaussian input and a large number of tap weights, approximate difference equations are also proposed. Experiment with simulations and theoretical calculations of filter convergence demonstrates good agreement between simulations and theory, proving the validity of the analysis.

Design techniques and performance of an LSI-based 2B1Q transceiver

Examines a 2B1Q transceiver system which was selected as the standard for an ISDN (integrated services digital network) loop transmission systems in the US. An LSI-based 2B1Q transceiver consisting of three LSI chips has been developed. Echo tail suppression, receiver design to configure stable decision feedback equalizer (DFE) operation and to improve NEXT performance, and the accurate analog front end circuit are discussed. Experimental results show that satisfactory performance is obtained.<<ETX>>

A novel adaptive step size control algorithm for adaptive filters

A novel adaptive step size control algorithm is proposed, in which the step size is approximated to the theoretically optimum value via leaky accumulators, realizing quasi-optimal control. The algorithm is applicable to most of the known tap weight adaptation algorithms. Analysis yields a set of difference equations for theoretically calculating expected filter convergence, and derives residual mean squared error (MSE) after convergence in a formula explicitly solved. Experiments with some examples prove that the proposed algorithm is highly effective in improving the convergence rate. The theoretically calculated convergence is shown to be in good agreement with that obtained through simulations.

A new efficient method of convergence calculation for adaptive filters using the sign algorithm with digital data inputs

This paper proposes a new method of theoretical calculation of the expected convergence process for adaptive filters using the sign algorithm with digital data as the input reference signal. In the analysis use is made of the Gaussian approximated conditional PDFf of the error signal to derive a set of difference equations. The results of the experiment show the sufficient accuracy of the proposed method for practical use, while significantly reducing the computing time in comparison with the previous methods.

Optimum Binary to Symbol Coding for 6PSK and Bit Error Rate Performance

This paper proposes the optimum binary to symbol coding for 6PSK (senary PSK) that minimizes bit errors. 6PSK is expected to be able to transmit data at a higher information rate than QPSK with lower required Eb/N0 compared with 8PSK. In this paper, after discussing general principles of 6PSK, the authors propose the optimum 5 bit to 2 symbol coding for 6PSK (2.5bit/symbol). The authors derive a theoretical formula for bit error rate (BER) versus Eb/N0 and simulate performance with and without differential coding. The required Eb/N0 for 6PSK is 1.4dB less than that for 8PSK at BER of 10-6. Penalty of differential coding is estimated about 0.2dB at BER of 10-6. The authors study BER performance of 6PSK in the presence of transmitter nonlinearity. 6PSK still outperforms 8PSK even for a small value of output back-off (OBO).

Normalized correlation-newton algorithm with variable control of q-norm

This paper proposes a new adaptation algorithm named normalized correlation-Newton (NC-Newton) algorithm and a novel variable q-norm control method (NC-Newton-Varq-norm) for complex-domain adaptive filters. First, stochastic models are presented for two types of impulse noise intruding adaptive filters: one is present in observation noise and another at filter input. After reviewing q-norm and NC-Newton algorithm, we propose a variable q-norm control method. Analysis of the NC-Newton-Varq-norm algorithm is developed for theoretically calculating transient and steady-state convergence behavior. Through experiment with some examples, we demonstrate effectiveness of the proposed variable q-norm control method in improving filter convergence speed while preserving robustness of the NC-Newton algorithm in impulsive noise environments. Good agreement between simulated and theoretical convergence behavior validates the analysis.

Transient Analysis of Complex-Domain Adaptive Threshold Nonlinear Algorithm (c-ATNA) for Adaptive Filters in Applications to Digital QAM Systems

The paper presents an adaptive algorithm named adaptive threshold nonlinear algorithm for use in adaptive filters in the complex-number domain (c-ATNA) in applications to digital QAM systems. Although the c-ATNA is very simple to implement, it makes adaptive filters highly robust against impulse noise and at the same time it ensures filter convergence as fast as that of the well-known LMS algorithm. Analysis is developed to derive a set of difference equations for calculating transient behavior as well as steady-state performance. Experiment with simulations and theoretical calculations for some examples of filter convergence in the presence of Contaminated Gaussian Noise demonstrates that the c-ATNA is effective in combating impulse noise. Good agreement between simulated and theoretical convergence proves the validity of the analysis.