Generalized correlation decomposition applied to array processing in unknown noise environments
Advances in spectrum analysis and array processing (vol. III)
Subspace methods for the blind identification of multichannel FIRfilters
IEEE Transactions on Signal Processing
Space-time fading channel estimation and symbol detection inunknown spatially correlated noise
IEEE Transactions on Signal Processing
Fast maximum likelihood for blind identification of multiple FIRchannels
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
OFDM channel estimation in the presence of interference
IEEE Transactions on Signal Processing
Optimal training design for MIMO OFDM systems in mobile wireless channels
IEEE Transactions on Signal Processing
A blind multichannel identification algorithm robust to orderoverestimation
IEEE Transactions on Signal Processing
How much training is needed in multiple-antenna wireless links?
IEEE Transactions on Information Theory
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Blind channel identification techniques based on second-order statistics (SOS) of the received data have been a topic of active research in recent years. Among the most popular is the subspace method (SS) proposed by Moulines et al. (1995). It has good performance when the channel output is corrupted by white noise. However, when the channel noise is correlated and unknown as is often encountered in practice, the performance of the SS method degrades severely. In this paper, we address the problem of estimating FIR channels in the presence of arbitrarily correlated noise whose covariance matrix is unknown. We propose several algorithms according to the different available system resources: (1) when only one receiving antenna is available, by upsampling the output, we develop the maximum a posteriori (MAP) algorithm for which a simple criterion is obtained and an efficient implementation algorithm is developed; (2) when two receiving antennae are available, by upsampling both the outputs and utilizing canonical correlation decomposition (CCD) to obtain the subspaces, we present two algorithms (CCD-SS and CCD-ML) to blindly estimate the channels. Our algorithms perform well in unknown noise environment and outperform existing methods proposed for similar scenarios.