P Vandaele

Deterministic blind modulation-induced source separation for digital wireless communications

In this paper, we present a new simple deterministic blind source separation algorithm, which is based on modulating the same data symbol sequence with different code sequences and transmitting the resulting modulated data symbol sequences through different antennas. The algorithm does not exploit the finite alphabet property of the data symbols. As a result, no iterations are required, and convergence is not an issue. Instantaneous mixtures (frequency-flat fading), as well as convolutive mixtures (frequency-selective fading), can be handled. In the case of a convolutive mixture, the difficulties that occur when the users have unequal channel orders are avoided. Moreover, the proposed algorithm is robust against channel order underestimation.

Combining raised cosine windowing and per tone equalization for RFI mitigation in DMT receivers

Discrete multitone (DMT) offers an elegant way to achieve high capacity, dividing the spectrum into small bands and processing these individually. The per tone equalizer (PTEQ) optimizes the capacity for each band individually, thus optimizing the whole. However, it provides little protection against narrow band radio frequency interference (RFI), being spread over all tones because of the high side lobes of the DFT filter band used in the receiver. The use of windowing functions limits this noise spreading, but is difficult to combine with the PTEQ. This paper describes a method to combine the PTEQ with a raised cosine window, while keeping the complexity reasonable. Extensions to other windowing functions are also given.

Two deterministic blind channel estimation algorithms based on oblique projections

Abstract In this paper we consider the problem of deterministic blind channel estimation using oblique projections. Oblique projections allow to decompose a matrix into two non-orthogonal components. This property allows to develop two new blind channel estimation algorithms. The algorithms are robust to the spatial color of the additive noise and they are deterministic: if there is no additive noise, they allow perfect channel recovery with a finite number of samples if the model assumptions hold.

An "SVD+Viterbi" algorithm for multiuser adaptive blind equalization of mobile radio channels

A fully adaptive algorithm for multiuser blind channel equalization is presented. The algorithm is based on an adaptive matrix singular value decomposition (SVD) for a (virtual) channel identification type operation and the Viterbi algorithm for subsequent symbol detection. Unlike other blind multiuser detection schemes that have appeared in the literature, the present algorithm removes multiple access interference (MAI) by exploiting the finite alphabet property of the input signals together with channel coding redundancy. The latter is believed to be a crucial new ingredient for performance in the context of MAI suppression.

A stochastic subspace algorithm for blind channel identification in noise fields with unknown spatial covariance

Abstract In this paper, the blind channel identification problem is formulated in a stochastic state space framework. Starting from a state space model, we introduce a preprocessing step based on two orthogonal subspace projections, derived from the theory of Van Overschee and De Moor (Subspace Identification for Linear Systems: Theory, Implementation, Applications, Kluwer Academic Publishers, Dordrecht, 1996). Using these orthogonal projections, we present an algorithm for blind channel estimation which is robust to the spatial color of the noise.

A non-iterative blind signal separation algorithm based on transmit diversity and coding

This paper presents a deterministic blind signal separation algorithm. The algorithm assumes transmit diversity, i.e. each user is equipped with at least two transmit antennas. Through each antenna the same symbol sequence is transmitted but coded in a different way. This coding does not require any extra bandwidth or power and allows one to remove multiple access interference (MAI) in a non-iterative way. The algorithm is applicable for any modulation format and forms an extension of the algorithm of Vandaele, Lens and Moonen (see Proceedings of the 2nd IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Annapolis, MD, USA, p.98-101, 1999) which is only applicable for BPSK modulation. We further demonstrate how coding combined with transmit diversity increases robustness to intersymbol interference (ISI) and order detection problems.

A stochastic subspace algorithm for blind channel identification in noise fields with unknown spatial color

The blind channel identification problem is formulated in a stochastic state space framework. Starting from a state space model we present a preprocessing step based on two orthogonal subspace projections. Using these orthogonal projections, we derive an algorithm for blind channel estimation which is insensitive to the spatial color of the noise. The performance of this new algorithm is demonstrated through simulation examples.

A recursive total least squares algorithm for deconvolution problems

Deconvolution problems are encountered in signal processing applications where an unknown input signal can only be observed after propagation through one or more noise corrupted FIR channels. The first step in recovering the input usually entails an estimation of the FIR channels through training based or blind algorithms. The standard procedure then uses least squares estimation to recover the input. A recursive implementation with constant computational cost is based on the Kalman filter. In this paper we focus on a total least squares based approach, which is more appropriate if errors are expected both on the output samples and the estimates of the FIR channels. We will develop a recursive total least squares algorithm (RTLS) which closely approximates the performance of the non-recursive TLS algorithm and this at a much lower computational cost.

A non-iterative blind binary signal separation algorithm based on linear coding

In this paper we present a new blind signal separation algorithm for data symbols belonging to a real constellation, e.g. BPSK. The algorithm is based on a direct estimation of the transmitted symbol sequences without identifying the mixing matrix. We show that for a real constellation, a simple linear coding scheme allows for a non-iterative separation. The coding scheme maintains the information rate without increasing the bandwidth.

Implementation of an RTLS blind equalization algorithm on DSP

Blind equalization has been a very active area of research during the last years. Research is mostly focused on performance without too much attention on the complexity of the presented techniques. However, the high complexity of these blind algorithms coupled with the high data rates of mobile telecommunications may hamper a practical implementation. Recently we presented a recursive total least squares (RTLS) algorithm which has a reduced computational complexity (P. Vandaele and M. Moonen, 1997). We integrate this algorithm into a transmitter/receiver structure and present some results on the implementation of the algorithm in DSP.