Dieter Boss

Generalized eigenvector algorithm for blind equalization

Abstract In 1994, an eigenvector solution to the problem of blind equalization of possibly mixed-phase linear time-invariant transmission channels was published in this journal. Unfortunately, this solution is ambiguous on a certain condition. In this paper, we introduce a novel iterative method termed Eigen Vector Algorithm for blind equalization (EVA), which not only overcomes the uniqueness problem, but also ensures, after some iterations, optimum linear equalization from few samples of the received signal. In the second part of the paper, the eigenvector solution is generalized to multiple output channels. The resulting algorithm, called GenEVA (Generalized EVA), can be applied to the iterative adjustment of (i) multiple parallel symbol-rate FIR equalizers, (ii) fractional tap spacing FIR equalizers, (iii) non-linear decision-feedback and (iv) time-variant FIR equalizers. Extensive simulation results illustrate the exceptional capabilities of g en eva.

Is blind channel estimation feasible in mobile communication systems? A study based on GSM

We compare the effect of blind and nonblind channel estimates on the performance of Global System for Mobile communications (GSM) receivers. More precisely, we investigate whether two blind approaches, based on higher order statistics (HOS), can compete with two conventional methods, exploiting training sequences. For blind and nonblind estimates of six fast and slowly fading mobile radio channels, we give simulated bit error rates (BERs), after Viterbi detection, in terms of the signal-to-noise ratio (SNR). We also study the influence of cochannel interferers at different values of the signal-to-interference ratio (SIR). Averaged over the six channel examples, we demonstrate that the blind channel estimation algorithm eigenvector approach to blind identification (EVI) leads to an SNR loss of 1.2-1.3 dB only, while it saves the 22% overhead in GSM data rate caused by the transmission of training sequences. Since just 142 samples are used for blind channel estimation, we consider this performance outstanding for an approach based on HOS.

Eigenvector algorithm for blind MA system identification

Abstract We present a novel approach to the blind estimation of a linear time-invariant possibly mixed-phase moving average (MA) system (channel) based on second and fourth order statistics of the stationary received signal. As the algorithm incorporates the solution of an eigenvector problem, it is termed E igen V ector approach to blind I dentification (EVI). One of EVI’s main features is its ability to obtain reliable estimates of the channel’s MA parameters on the basis of very short records of received data samples. It is also robust with respect to an overestimation of the channel order. Furthermore, we demonstrate that, if independent additive white Gaussian noise is present, the degradation of the MA parameter estimates is minor even at low signal-to-noise ratios. By simulation results, we finally show the potential applicability of EVI to mobile radio communication channels under time-invariance conditions typically assumed in GSM receivers.

Impact of blind versus non-blind channel estimation on the BER performance of GSM receivers

We investigate in this paper whether the HOS-based blind channel estimation method EVI (eigenvector approach to blind identification) can compete with the nonblind cross-correlation-based scheme used in state-of-the-art GSM receivers (Global System for Mobile communication). For blind, non-blind, and ideal estimates of COST-207 mobile radio channels, we give simulated bit error rates (BER) after Viterbi detection in terms of the mean signal-to-noise ratio (SNR). Averaged over three COST-207 propagation environments, EVI leads to a mean SNR loss of 1.2 dB only, while it saves the 22% overhead in the GSM data rate due to the transmission of training sequences. Since just 142 samples are used for channel estimation, we consider this performance outstanding for an approach based on HOS.

Blind GSM channel estimation

The bandwidth efficiency of many communication systems could be improved if the transmission channel was estimated blindly, i.e. without resort to training sequences. As an example, we investigate in this paper the applicability of two algorithms for the blind identification of mixed-phase linear time-invariant FIR systems to the estimation of mobile radio channels on GSM conditions. Both approaches exploit higher order statistics (HOS) of the received signal sampled at symbol rate. Although this class of algorithms is said to require an excessive number of samples to achieve acceptable performance levels, we demonstrate that it is possible with the eigenvector approach to blind identification (EVI) to blindly estimate realistic COST-207 channels from one GSM burst (142 samples). At a signal-to-noise ratio of 7 dB, all sample channels are identified within a normalized mean square error bound of 5 per cent.

Blind GSM channel estimation based on higher order statistics

The performance of many communication systems could be improved if the transmission channel was estimated blindly, i.e. without training sequences. As an example, we investigate whether, on GSM conditions, the blind channel estimation method EVI (eigen vector approach to blind identification) can compete with the non-blind least squares scheme based on the cross-correlation. For Gaussian stationary uncorrelated scattering channels, we give simulated bit error rates (BER) after Viterbi detection in terms of the mean signal-to-noise ratio (S~N~R~) for blind, non-blind, and ideal channel estimation. Averaged over three COST-207 propagation environments, EVI leads to an S~N~R~ loss of 1.1 dB only, which is quite remarkable for an approach based on higher order statistics, as just 142 samples can be used for blind channel estimation.

Blind GSM channel estimation under channel coding conditions

As a fundamental component of the global system for mobile communications (GSM), channel coding aims at improving speech and data transmission quality when the signal encounters disturbances. We put the main emphasis on investigating the application of blind channel estimation approaches based on second order statistics and/or higher order statistics to the identification of a full rate data traffic channel (TCH/F9.6). Giving the bit error rates after equalization and channel decoding in terms of the E~/sub b//N/sub 0/ ratio, we show that there are blind channel estimation algorithms which are almost as efficient as non-blind methods even if the disturbance is non-Gaussian.