A channel subspace post-filtering approach to adaptive least-squares estimation
A major challenge while communicating in dynamic channels, such as the underwater acoustic channel, is the large amount of time-varying inter-symbol interference (ISI) due to multipath. In many realistic channels, the fluctuations between different taps of the sampled channel impulse response are co...
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| Veröffentlicht in: | IEEE transactions on signal processing 2004-07, Vol.52 (7), p.1901-1914 |
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| container_end_page | 1914 |
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| container_issue | 7 |
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| container_title | IEEE transactions on signal processing |
| container_volume | 52 |
| creator | Nadakuditi, R. Preisig, J.C. |
| description | A major challenge while communicating in dynamic channels, such as the underwater acoustic channel, is the large amount of time-varying inter-symbol interference (ISI) due to multipath. In many realistic channels, the fluctuations between different taps of the sampled channel impulse response are correlated. Traditional least-squares algorithms used for adapting channel equalizers do not exploit this correlation structure. A channel subspace post-filtering algorithm is presented that treats the least-squares channel estimate as a noisy time series and exploits the channel correlation structure to reduce the channel estimation error. The improvement in performance of the post-filtered channel estimator is predicted theoretically and demonstrated using both simulation and experimental data. Experimental data is also used to demonstrate the improvement in performance of a channel estimate-based decision feedback equalizer that uses this post-filtered channel estimate to determine the equalizer coefficients. |
| doi_str_mv | 10.1109/TSP.2004.828926 |
| format | Article |
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In many realistic channels, the fluctuations between different taps of the sampled channel impulse response are correlated. Traditional least-squares algorithms used for adapting channel equalizers do not exploit this correlation structure. A channel subspace post-filtering algorithm is presented that treats the least-squares channel estimate as a noisy time series and exploits the channel correlation structure to reduce the channel estimation error. The improvement in performance of the post-filtered channel estimator is predicted theoretically and demonstrated using both simulation and experimental data. 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| ispartof | IEEE transactions on signal processing, 2004-07, Vol.52 (7), p.1901-1914 |
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| source | IEEE Electronic Library (IEL) |
| subjects | Algorithm design and analysis Algorithms Applied sciences Channels Correlation Covariance matrix Decision feedback equalizers Detection, estimation, filtering, equalization, prediction Equalizers Estimates Exact sciences and technology Filtering algorithms Fluctuations Impulse response Information, signal and communications theory Intersymbol interference Least squares method Multipath channels Oceans Signal and communications theory Signal processing algorithms Signal, noise Subspaces Telecommunications and information theory Underwater acoustics |
| title | A channel subspace post-filtering approach to adaptive least-squares estimation |
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