Further Results On Blind Identification And Equalization Of Multiple Fir Channels
In previous work, we have shown that in the case of multiple antennas and/or oversampling, FIR ZF equalizers exist for FIR channels and can be obtained from the noise-free linear prediction (LP) problem. The LP problem also lead to a minimal parameterization of the noise subspace, which was used to...
| Main Authors: | , , |
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| Format: | Text |
| Language: | English |
| Published: |
1995
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| Online Access: | http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.44.7241 http://www-isl.stanford.edu/people/papadias/papers/icassp95paper.ps.gz |
| Summary: | In previous work, we have shown that in the case of multiple antennas and/or oversampling, FIR ZF equalizers exist for FIR channels and can be obtained from the noise-free linear prediction (LP) problem. The LP problem also lead to a minimal parameterization of the noise subspace, which was used to solve the deterministic maximum likelihood (DML) channel estimation problem. Here we present further contributions along two lines. One is a number of blind equalization techniques of the adaptive filtering type. We also present some robustifying modifications of the DML problem. 1. INTRODUCTION Consider linear digital modulation over a linear channel with additive Gaussian noise so that the received signal can be written as y(t) = X k akh(t \Gamma kT ) + v(t) (1) where the ak are the transmitted symbols, T is the symbol period, h(t) is the (overall) channel impulse response. The cyclostationarity of fy(t)g and the fact that after sampling, multiple received signals from multiple ante. |
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