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