Blind identifiability of certain classes of multipath channels from second-order statistics using antenna arrays

Second-order blind identifiability of certain classes of multipath channels using antenna arrays

Recently, a number of classes of multipath channels which are not blindly identifiable from fractionally spaced samples and second-order cyclic spectra have been presented. In this paper, we consider the blind identification problem of these channels using multiple antennas and show that they will not in general give rise to any common roots among the sub-channels formed from the antennas, and hence, they can be identified from second-order statistics.

Blind Identification of Multipath Channels: A

2001

In this paper, blind identification of single-input multiple-output (SIMO) systems using second-order statistics (SOS) only is considered. Using the assumption of a specular multipath channel, we investigate a parametric variant of the so-called subspace method. Nonparametric subspace-based methods require a precise estimation of the model order; overestimation of the model order leads to inconsistent channel estimates. We show that the parametric subspace method gives consistent channel estimates when only an upper bound of the channel order is known. A new algorithm, which exploits parametric information on the channel structure, is presented. A statistical performance analysis of the proposed parametric subspace criterion is presented; limited Monte Carlo experiments show that the proposed algorithm is second-order optimal for a large class of channels.

Blind identification of multipath channels: a parametric subspace approach

IEEE Transactions on Signal Processing, 2001

Blind channel identification based on cyclic statistics

IEE Proceedings - Radar, Sonar and Navigation, 1998

Use of cyclic statistics in fractionally sampled channels in subspace fitting and linear prediction for channel identification is proposed, possibly for multiuser and multiple antennas. Identification schemes are based on cyclic statistics using the stationary multivariate representation, leading to the use of all cyclic statistics. Compared with classical approaches, the methods proposed have an equivalent performance for subspace fitting, and an enhanced performance for linear prediction.

Blind channel identification based on second-order statistics: a frequency-domain approach

IEEE Transactions on Information Theory, 1995

In this communication, necessary and sufficient conditions are presented for the unique blind identification of possibly nonminimum phase channels driven by cyclostationary processes. Using a frequencydomain formulation, it is first shown that a channel can be identified by the second-order statistics of the observation if and only if the channel transfer function does not have special uniformly spaced zeros. This condition leads to several necessary and sufficient conditions on the observation spectra and the channel impulse response. Based on the frequency-domain formulation, a new identification algorithm is proposed.

Blind identification of sparse multipath channels using cyclostationary statistics

9th European Signal Processing Conference (EUSIPCO 1998), 1998

Blind identification of a wireless communication channel is an important issue in communication system design. Most existing blind system identification techniques process the unknown information of the system from its output only. However, in many practical situation partial knowledge of the system transfer function is available. By relying on this known information, the performance of channel identification and equalization can be significantly enhanced. In this paper, we introduce a new system identification technique that exploits both the a priori knowledge of the pulse shape filter and the multi-path channel propagation model. The approach consists first in processing the cyclo-spectrum of the system output that is shown to be superimposed exponential function of the channel propagation delays and attenuations. Then, the frequency parameters, i.e., channel propagation parameters, are later estimated using the Matrix Pencils (MP) frequency estimation method [7].

Linear prediction and subspace fitting blind channel identification based on cyclic statistics

Proceedings of 13th International Conference on Digital Signal Processing, 1997

Blind channel identification and equalization based on second-order statistics by subspace fitting and linear prediction have received a lot of attention lately. On the other hand, the use of cyclic statistics in fractionally sampled channels has also raised considerable interest. We propose to use these statistics in subspace fitting and linear prediction for (possibly multiuser and multiple antennas) channel identification. We base our identification schemes on the cyclic statistics, using the stationary multivariate representation introduced by [2] and [4] . This leads to the use of all cyclic statistics. The methods proposed appear to have good performance. 3 7 7 7 5

On Blind MIMO System Identification Based on Second-Order Cyclic Statistics

Research Letters in Signal Processing, 2008

This letter introduces a new frequency domain approach for either MIMO System Identification or Source Separation of convolutive mixtures in cyclostationary context. We apply the joint diagonalization algorithm to a set of cyclic spectral density matrices of the measurements to identify the mixing system at each frequency up to permutation and phase ambiguity matrices. An efficient algorithm to overcome the frequency dependent permutations and to recover the phase, even for non-minimum-phase channels, based on cyclostationarity is also presented. The new approach exploits the fact that each input has a different and specific cyclic frequency. A comparison with an existing MIMO method is proposed.

A frequency domain-based approach for blind MIMO system identification using second-order cyclic statistics

Signal Processing, 2009

This article introduces a new frequency domain approach for either MIMO system identification or source separation of convolutive mixtures of cyclostationary signals. We apply the joint diagonalization algorithm to a set of cyclic spectral density matrices of the measurements to identify the mixing system at each frequency bin up to permutation and phase ambiguity matrices. An efficient algorithm to overcome the frequency-dependent permutations and to recover the phase, even for non-minimumphase channels, based on cyclostationarity is also presented. The new approach exploits the fact that each input signal has a different and specific cyclic frequency. Simulation examples are presented to illustrate the effectiveness of this approach.

Blind identifiability of certain classes of multipath channels from second-order statistics using antenna arrays (original) (raw)