Martin Kristensson

Blind subspace identification of a BPSK communication channel

This paper considers the problem of blind estimation of multiple FIR channels. When a subspace algorithm is applied to the blind identification problem, incorporating information about the symbol constellation is in general not possible. However, by exploiting special properties of one dimensional symbol constellations (BPSK), it is shown that it is possible to improve or simplify a class of algorithms for blind channel identification. It is also shown that in the case of one dimensional symbol constellations there is a third way, apart from multiple antennas and oversampling, of arriving at a multichannel representation of the communication system.

Radio resource management in roaming based multi-operator WCDMA networks

Infrastructure sharing and mobile virtual network operators (MVNO) are becoming more and more common in todays mobile networks. In both cases, the subscribers of multiple operators connect to the same radio access network via, e.g., roaming based methods. Depending on how much each operator pays, they should then be guaranteed a certain capacity in the shared network. The paper discusses different solutions for how the radio resources in such a roaming based multi-operator WCDMA network may be allocated to the sharing operators. One particular method based on radio resource management (RRM) with nonpreemptive priority queuing in the admission control is presented in detail. The method seems to provide an attractive tradeoff between fairness and total system capacity.

A statistical approach to subspace based blind identification

Blind identification of single input multiple output systems is considered herein. The low-rank structure of the output signal is exploited to blindly identify the channel using a subspace fitting framework. Two approaches based on a minimal linear parameterization of a subspace are presented and analyzed. The asymptotically best consistent estimate is derived for the class of blind subspace-based techniques. The asymptotic estimation error covariance of the subspace estimates is derived, and the corresponding covariance of the statistically optimal estimates provides a lower bound on the estimation error covariance of subspace methods. A two-step procedure involving only linear systems of equations is presented that asymptotically achieves the bound. Simulations and numerical examples are provided to compare the two approaches.

Further results and insights on subspace based sinusoidal frequency estimation

Subspace-based methods for parameter identification have received considerable attention in the literature. Starting with a scalar-valued process, it is well known that subspace-based identification of sinusoidal frequencies is possible if the scalar valued data is windowed to form a low-rank vector-valued process. MUSIC and ESPRIT-like estimators have, for some time, been applied to this vector model. In addition, a statistically attractive Markov-like procedure for this class of methods has been proposed. Herein, the Markov-like procedure is reinvestigated. Several results regarding rank, performance, and structure are given in a compact manner. The large sample equivalence with the approximate maximum likelihood method by Stoica et al. (1988) is also established.

Modified IQML and weighted subspace fitting without eigendecomposition

This paper deals with direction estimation of signals impinging on a uniform linear sensor array. A well-known algorithm for this problem is IQML. However, estimates computed with IQML are in general biased, especially in noisy scenarios. We propose a modification of IQML (MIQML) that gives consistent estimates at approximately the same computational cost. In addition, an algorithm (WSF-E) with an estimation error covariance which is asymptotically identical to the asymptotic Cramer–Rao lower bound is presented. The WSF-E algorithm resembles weighted subspace fitting (WSF) or MODE, but achieves optimal performance without having to compute an eigendecomposition of the sample covariance matrix.

Statistical analysis of a subspace method for blind channel identification

This paper considers the problem of blind channel estimation of multi-channel FIR filters. This is a problem arising in, for example, mobile communication systems using digital signalling. By using the orthogonality property between the noise subspace and the channel matrix, it has been shown in earlier work that the channel matrix is identifiable up to a multiplicative constant. In this article, the asymptotic properties of a subspace method using this orthogonality property is presented. An asymptotically correct weighting matrix is derived, demonstrating an attainable lower theoretical bound using the subspace estimate.

Robust blind second-order deconvolution

Second-order blind deconvolution of single input multiple output (SIMO) finite impulse response (FIR) channels is considered. A major drawback of several blind diversity techniques using antenna arrays/temporal-oversampling is high sensitivity to the choice of the model order. In this contribution, a robust method using only the second-order statistics is described. It provides high and robust estimation accuracy even for a limited sample size and an unknown model order. In contrast to other suggested approaches, that often exploit properties valid only in the large sample case, the proposed method is applicable both in the large sample and high signal-to-noise ratio (SNR) scenario.

Asynchronous DS-CDMA detectors robust to timing errors

An asynchronous DS-CDMA system with timing errors is considered. Two different approaches for dealing with the problem are presented. The first method is based on robustifying the MMSE estimator against timing errors. The second approach models the timing errors as an extra noise term and formulates the best linear unbiased estimator of the data symbols. Simulations of the bit error rate indicate that the two approaches have similar performance. However, the difference to non-robustified algorithms is large.

Modified IQML and a statistically efficient method for direction estimation without eigendecomposition

This paper deals with direction estimation of signals impinging on a uniform linear sensor array. A well known algorithm for this problem is iterative quadratic maximum likelihood (IQML). Unfortunately, the IQML estimates are in general biased, especially in noisy scenarios. We propose a modification of IQML (MIQML) that gives consistent estimates at approximately the same computational cost. In addition, an algorithm with an estimation error covariance which is asymptotically identical to the asymptotic Cramer-Rao lower bound is presented. The optimal algorithm resembles weighted subspace fitting or MODE, but achieves optimal performance without having to compute an eigendecomposition of the sample covariance matrix.

Some results on blind deconvolution applied to digital communication signals

Blind deconvolution techniques applied to spatially and/or temporally oversampled signals have attracted much interest in the research community. This contribution contains an analysis of experimental data collected from an antenna array in a suburban environment. The noise subspace (NS) technique of Moulines et al. (1995) and the linear prediction method of Abed-Meraim et al. (1995) and Gorokhov et al. (1996) are examined. The real data examples presented demonstrate a case where joint spatial and temporal deconvolution has clear benefits as compared to decoupled processing.