Gilles Burel

Optimal minimum distance-based precoder for MIMO spatial multiplexing systems

We describe a new precoder based on optimization of the minimum Euclidean distance d/sub min/ between signal points at the receiver side and for use in multiple-input multiple-output (MIMO) spatial multiplexing systems. Assuming that channel state information (CSI) can be made available at the transmitter, the three steps ( noise whitening, channel diagonalization and dimension reduction), which are currently used in investigations on MIMO systems, are performed. Thanks to this representation, an optimal d/sub min/ precoder is derived in the case of two different transmitted data streams. For quadrature phase-shift keying (QPSK) modulation, a numerical approach shows that the precoder design depends on the channel characteristics. Comparisons with maximum signal-to-noise ratio (SNR) strategy and other precoders based on criteria, such as water-filling (WF), minimum mean square error (MMSE), and maximization of the minimum singular value of the global channel matrix, are performed to illustrate the significant bit-error-rate (BER) improvement of the proposed precoder.

Blind estimation of the pseudo-random sequence of a direct sequence spread spectrum signal

Direct sequence spread spectrum transmissions (DS-SS) are now widely used for secure communications, as well as for multiple access. They have many interesting properties, including low probability of interception. Indeed, DS-SS transmitters use a periodical pseudo-random sequence to modulate the baseband signal before transmission. A receiver which does not know the sequence cannot demodulate the signal. In this paper, we propose a method which can estimate the spreading sequence in a noncooperative context. The method is based on eigenanalysis techniques. The received signal is divided into windows, from which a covariance matrix is computed. We show that the sequence can be reconstructed from the two first eigenvectors of this matrix, and that useful information, such as the desynchronization time, can be extracted from the eigenvalues. Experimental results show that the method can provide a good estimation, even when the received signal is far below the noise level.

Three-dimensional invariants and their application to object recognition

Abstract Although recognition of objects from 2D projections (i.e. images) has been widely studied among the image processing community, little research has been devoted to recognition using 3D information. A general approach for deriving 3D invariants is proposed in this paper. These invariants can be used as input to a statistical classifier, such as a k-nearest-neighbours algorithm or a neural network. The approach consists of decomposing the object onto an orthonormal basis composed of the eigenvectors of the angular momentum operator from quantum mechanics. Then, using Clebsch-Gordan coefficients, contravariant tensors of order 1 are constructed, and 3D invariants are obtained by tensor contraction. The approach offers an alternative to structural methods for 3D object description and recognition. Experimental results are provided to illustrate the method.

Blind Recognition of Linear Space–Time Block Codes: A Likelihood-Based Approach

Blind recognition of communication parameters is a research topic of high importance for both military and civilian communication systems. Numerous studies about carrier frequency estimation, modulation recognition as well as channel identification are available in literature. This paper deals with the blind recognition of the space-time block coding (STBC) scheme used in multiple-input-multiple-output (MIMO) communication systems. Assuming there is perfect synchronization at the receiver side, this paper proposes three maximum-likelihood (ML)-based approaches for STBC classification: the optimal classifier, the second-order statistic (SOS) classifier, and the code parameter (CP) classifier. While the optimal and the SOS approaches require ideal conditions, the CP classifier is well suited for the blind context where the communication parameters are unknown at the receiver side. Our simulations show that this blind classifier is more easily implemented and yields better performance than those available in literature.

Detection and localization of faces on digital images

Abstract A method for automatic detection and localization of faces on digital images is proposed. The method is based on learning by example and multi-resolution analysis of digital images. Special emphasis is put on the management of the learning data, in order to improve the performances. Various experimental results, obtained by using a Multi-Layer Perceptron (MLP) as a classifier, are provided.

Determination of the orientation of 3D objects using spherical harmonics

The paper describes a method for estimation of the orientation of 3D objects without point correspondence information. It is based on decomposition of the object onto a basis of spherical harmonics. Tensors are obtained, and their normalization provides the orientation of the object. Theoretical and experimental results show that the approach is more accurate than the classical method based on the diagonalization of the inertia matrix. Fast registration of 3D objects is a problem of practical interest in domains such as robotics and medical imaging, where it helps to compare multimodal data.

Extension of the MIMO Precoder Based on the Minimum Euclidean Distance: A Cross-Form Matrix

Under full channel state information at the transmitter side (Tx-CSI), MIMO precoders can be designed by the optimization of many pertinent criteria, like, for example, the maximizing post-processing signal-to-noise ratio (max-SNR or beamforming solution), or the minimizing weighted mean square error between transmit and receive vector-symbols (W-MMSE solution). These solutions decouple the MIMO channel into b parallel independent datastreams. This diagonal structure reduces the complexity of the maximum likelihood (ML) decisions but the diversity order of these schemes is limited. Recently, we proposed a precoder, max-dmjn solution, which optimizes the exact expression of the minimum Euclidean distance and leads to a non diagonal structure allowing to achieve maximum diversity order. However, the result is available only for two transmit datastreams (6 = 2) and BPSK and QPSK modulations. In this paper, we propose a heuristic method to deal with the case b > 2, which provides a suboptimal, but good solution to this general problem. The new precoder, Equal-dm;n (E-dmin), is based on a non diagonal cross-form structure. It significantly enhances the transmit diversity in the eigen-subchannels. We demonstrate that the achieved diversity order is greater than that of precoders with diagonal structure for the same number of datastreams despite a tradeoff between rate and diversity. This design can also ensure quality of service (QoS) by using an adapted power allocation strategy. Performance comparisons show the BER improvement for MIMO and MIMO-OFDM systems.

Hierarchical Space-Time Block Code Recognition Using Correlation Matrices

The blind recognition of communication parameters is a key research issue for commercial and military communication systems. The results of numerous investigations about symbol timing estimation, modulation recognition as well as identification of the number of transmitters have been reported in the literature. But, to our knowledge, none of them have dealt with the recognition of the Space-Time Block Codes (STBC) used in multiple transmitter communications. In order to blindly recognize the STBC of a wireless communication, this paper proposes a method based on the space-time correlations of the received signals. Under perfect timing synchronization and under ideal conditions (full rank channel and a number of receivers greater or equal to the number of transmitters), it shows that the Frobenius norms of these statistics present non-null values whose positions only depend on the STBC at the transmitter side. A classifier for the space-time code recognition of 5 linear STBC (Spatial Multiplexing, Alamouti Coding, and 3 Orthogonal STBC using 3 antennas) is presented. Simulations show that the proposed method performs well even at low signal-to-noise ratios.

Blind Channel Estimation for STBC Systems Using Higher-Order Statistics

This paper describes a new blind channel estimation algorithm for Space-Time Block Coded (STBC) systems. The proposed method exploits the statistical independence of sources before space-time encoding. The channel matrix is estimated by minimizing a kurtosis-based cost function after Zero-Forcing equalization. In contrast to subspace or Second-Order Statistics (SOS) approaches, the proposed method is more general since it can be employed for the general class of linear STBCs including Spatial Multiplexing, Orthogonal, quasi-Orthogonal and Non-Orthogonal STBCs. Furthermore, unlike other approaches, the method does not require any modification of the transmitter and, consequently, is well-suited for non-cooperative context. Numerical examples corroborate the performance of the proposed algorithm.

Minimum BER diagonal precoder for MIMO digital transmissions

We propose a minimum bit error rate (MBER) diagonal precoder for multi-input multi-output (MIMO) transmission systems. This work is based on previous results obtained by Sampath et al. (IEEE ISPACS, Honolulu, Hawaii, 2000, p. 823) in which the global transmission system (precoder and equalizer) is optimized with the minimum mean square error (MMSE) criterion. This process leads to an interesting diagonality property which decouples the MIMO channel into parallel and independent data streams and allows to perform an easy maximum likelihood (ML) detection. This system is then optimized using a new diagonal precoder that minimizes the BER. Our work is motivated by the fact that, from a practical point of view, people are likely to prefer a system that minimizes the BER rather than the mean square error. The performance improvement is illustrated via Monte Carlo simulations using a quadratic amplitude modulation (QAM).