Structure-Based Subspace Method for Multi-Channel Blind System Identification

In this work, a novel subspace-based method for blind identification of multichannel finite impulse response (FIR) systems is presented. Here, we exploit directly the impeded Toeplitz channel structure in the signal linear model to build a quadratic form whose minimization leads to the desired channel estimation up to a scalar factor. This method can be extended to estimate any predefined linear structure, e.g. Hankel, that is usually encountered in linear systems. Simulation findings are provided to highlight the appealing advantages of the new structure-based subspace (SSS) method over the standard subspace (SS) method in certain adverse identification scenarii.Comment: 5 pages, Submitted to IEEE Signal Processing Letters, January 201

Blind MultiChannel Identification and Equalization for Dereverberation and Noise Reduction based on Convolutive Transfer Function

This paper addresses the problems of blind channel identification and multichannel equalization for speech dereverberation and noise reduction. The time-domain cross-relation method is not suitable for blind room impulse response identification, due to the near-common zeros of the long impulse responses. We extend the cross-relation method to the short-time Fourier transform (STFT) domain, in which the time-domain impulse responses are approximately represented by the convolutive transfer functions (CTFs) with much less coefficients. The CTFs suffer from the common zeros caused by the oversampled STFT. We propose to identify CTFs based on the STFT with the oversampled signals and the critical sampled CTFs, which is a good compromise between the frequency aliasing of the signals and the common zeros problem of CTFs. In addition, a normalization of the CTFs is proposed to remove the gain ambiguity across sub-bands. In the STFT domain, the identified CTFs is used for multichannel equalization, in which the sparsity of speech signals is exploited. We propose to perform inverse filtering by minimizing the $\ell_1$-norm of the source signal with the relaxed $\ell_2$-norm fitting error between the micophone signals and the convolution of the estimated source signal and the CTFs used as a constraint. This method is advantageous in that the noise can be reduced by relaxing the $\ell_2$-norm to a tolerance corresponding to the noise power, and the tolerance can be automatically set. The experiments confirm the efficiency of the proposed method even under conditions with high reverberation levels and intense noise.Comment: 13 pages, 5 figures, 5 table

Cdma blind channel equalization: a weighted subsface a proach

This paper considers the problem of blind demodulation of multiuser information symbols in a direct-sequence code-division multiple access (DS-CDMA) environment. Channel estimation and symbol detection in the presence of both multiple access interference (MAI) and intersymbol interference (ISI) is carried out with second order statistics methods from the received data. This problem is similar to direction of arrival (DOA) estimation, where many solutions like the MUSIC algorithm orPeer ReviewedPostprint (published version

Blind multiuser detection using hidden markov models theory

We present an adaptive algorithm based on the theory of hidden Markov models (HMM) which is capable of jointly detecting the users in a DS-CDMA system. The proposed technique is near-far resistant and completely blind in the sense that no knowledge of the signature sequences, channel state information or training sequences is required for any user. In addition to this, an estimate of the signature of each user convolved with its physical channel impulse response (CIR), and an estimate of the background noise variance are provided once convergence is achieved (as well as estimated data sequences). At this moment, and using that CIR estimate, we can switch to any decision-directed (DD) adaptation scheme.Peer ReviewedPostprint (published version