Abstract
AbstractBlind 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.
Highlights
Channel distortion remains one of the hurdles in highfidelity data communications because the performance of a digital communication system is invariably affected by the characteristics of the channel over which the signals are transmitted as well as by additive noise
Using computer simulations, we examine the performance of our channel estimation algorithms (MAP, canonical correlation decomposition (CCD)-SS, and CCD-Maximum likelihood (ML)) and compare their performance with that of the two subspace methods: the SS [1] and modified subspace (MSS) [10] under different signal-to-noise ratio (SNR)
We address the important practical problem of FIR channel estimation in unknown correlated noise environments
Summary
Channel distortion remains one of the hurdles in highfidelity data communications because the performance of a digital communication system is invariably affected by the characteristics of the channel over which the signals are transmitted as well as by additive noise. Since spatial correlation of noise is negligible when the two receiving antennae are separated by more than a few wavelengths of the transmission carrier [13], a condition not hard to satisfy in the case of a base station, we assume in this paper, that the noise vectors from the two antennae are uncorrelated while the temporal correlation of the individual noise vector still maintains For this case, we employ the canonical correlation decomposition (CCD) [14, 15] for identifying the subspaces and forming the corresponding projectors, and develop a subspace-based algorithm (CCD-SS) and a maximum likelihood-based algorithm (CCD-ML) for the estimation of the channel. Computer simulations show that all these methods achieve superior performance to the MSS method under different signal-to-noise ratio (SNR)
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