Adaptive Blind Channel Identification Research Articles

A new adaptive blind channel identification algorithm

This paper addresses the blind identification of single-input multiple-output (SIMO) finite-impulse-response (FIR) systems. We first propose a new adaptive algorithm for the blind identification of SIMO FIR systems. Then, its convergence property is analyzed systematically. It is shown that under some mild conditions, the proposed algorithm is guaranteed to converge in the mean to the true channel impulse responses in both noisy and noiseless cases. Simulations are carried out to demonstrate the theoretical results.

Generalized Optimal Step-Size for Blind Multichannel LMS System Identification

The choice of step-size in adaptive blind channel identification using the multichannel least mean squares (MCLMS) algorithm is critical and controls its convergence rate, stability, and sensitivity to noise. In this letter, we derive the expression for an optimal step-size in the Wiener sense and investigate its properties. An implementation technique for the Wiener solution of the self-adaptive step-size is presented, and it is shown that significant performance improvements are obtained compared to existing approaches in the presence of noise

A class of frequency-domain adaptive approaches to blind multichannel identification

We extend our previous studies on adaptive blind channel identification from the time domain into the frequency domain. A class of frequency-domain adaptive approaches, including the multichannel frequency-domain LMS (MCFLMS) and constrained/unconstrained normalized multichannel frequency-domain LMS (NMCFLMS) algorithms, are proposed. By utilizing the fast Fourier transform (FFT) and overlap-save techniques, the convolution and correlation operations that are computationally intensive when performed by the time-domain multichannel LMS (MCLMS) or multichannel Newton (MCN) methods are efficiently implemented in the frequency domain, and the MCFLMS is rigorously derived. In order to achieve independent and uniform convergence for each filter coefficient and, therefore, accelerate the overall convergence, the coefficient updates are properly normalized at each iteration, and the NMCFLMS algorithms are developed. Simulations show that the frequency-domain adaptive approaches perform as well as or better than their time-domain counterparts and the cross-relation (CR) batch method in most practical cases. It is remarkable that for a three-channel acoustic system with long impulse responses (256 taps in each channel) excited by a male speech signal, only the proposed NMCFLMS algorithm succeeds in determining a reasonably accurate channel estimate, which is good enough for applications such as time delay estimation.

Adaptive blind channel identification algorithm based on linear prediction for SIMO FIR systems

An adaptive algorithm for blind identification of single-input multiple-output (SIMO) FIR systems is proposed. It is based on the one-step forward linear prediction (LP) technique and can be implemented by an RLS adaptation. Unlike most second-order statistics (SOS)-based approaches, the proposed solution does not require the computation of the correlation matrix or its inverse explicitly. The obtained results demonstrate that the proposed approach is able to deliver better performance compared with the typical batch algorithm. It is also observed that the proposed algorithm can tolerate the appearance of near common zeros among the subchannels.