Conference Paper

Blind Decoding of MISO-OSTBC Systems Based on Principal Component Analysis

Dept. of Commun. Eng., Cantabria Univ., Santander
DOI: 10.1109/ICASSP.2006.1661026 Conference: Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings. 2006 IEEE International Conference on, Volume: 4
Source: IEEE Xplore

ABSTRACT In this paper, a new second-order statistics (SOS) based method for blind decoding of orthogonal space time block coded (OSTBC) systems with only one receive antenna is proposed. To avoid the inherent ambiguities of this problem, the spatial correlation matrix of the source signals must be non-white and known at the receiver. In practice, this can be achieved by a number of simple linear precoding techniques at the transmitter side. More specifically, it is shown in the paper that if the source correlation matrix has different eigenvalues, then the decoding process can be formulated as the problem of maximizing the sum of a set of weighted variances of the signal estimates. Exploiting the special structure of OSTBCs, this problem can be reduced to a principal component analysis (PCA) problem, which allows us to derive computationally efficient batch and adaptive blind decoding algorithms. The algorithm works for any OSTBC (including the popular Alamouti code) with a single receive antenna. Some simulation results are presented to demonstrate the potential of the proposed procedure

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    ABSTRACT: In wireless communication systems, channel state information (CSI) acquisition is typically performed at the receiver side every time a new frame is received, without taking into account whether it is really necessary or not. Considering the special case of the 2 × 1 Alamouti orthogonal space-time block code, this work proposes to reduce computational complexity associated with the CSI acquisition by including a decision rule to automatically determine the time instants when CSI must be again updated. Otherwise, a previous channel estimate is reused. The decision criterion has a very low computational complexity since it consists in computing the cross-correlation between preambles sent by the two transmit antennas. This allows us to obtain a considerable reduction on the complexity demanded by both supervised and unsupervised (blind) channel estimation algorithms. Such preambles do not penalize the spectral efficiency in the sense they are mandatory for frame detection as well as for time and frequency synchronization in current wireless communication systems.
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    ABSTRACT: In this paper we consider the problem of blind estima-tion of time-varying multiple-input multiple-output (MIMO) channels under space-time block coded (STBC) transmis-sions. Firstly, the time-varying channel is deterministically represented by means of a basis expansion model (BEM), which reduces the number of parameters to be estimated. Secondly, the STBC structure is exploited to blindly re-cover the channel parameters by means of a subspace tech-nique, which reduces to the solution of a generalized eigen-value problem (GEV). Unlike previous approaches, the pro-posed method provides very accurate results even for non-orthogonal STBCs and high Doppler frequencies, which is illustrated by means of some numerical examples.
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    ABSTRACT: In many practical parameter estimation problems, the statis- tical properties of the sources can be exploited to improve the quality of the estimates. In this paper we consider the corre- lation and Kullback matching criteria (CM and KM respec- tively), which are applied to the problem of blind channel estimation under orthogonal space-time block coded (OS- TBC) transmissions. Specifically, it is shown that the special OSTBC structure provides straightforward closed form solu- tions, which reduce to the extraction of the principal eigen- vector of the observation correlation matrix modified by the code matrices and a set of weighting factors. Additionally, we prove that the KM technique is equivalent to the CM approach for low SNRs, and to the relaxed blind maximum likelihood (ML) decoder for high SNRs. Finally, the perfor- mance of the proposed techniques is illustrated by means of several simulation examples.

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