2017
Santos, Irene; Murillo-Fuentes, Juan Jose; Boloix-Tortosa, Rafael; Arias-de-Reyna, Eva; Olmos, Pablo M
Expectation Propagation as Turbo Equalizer in ISI Channels Artículo de revista
En: IEEE Transactions on Communications, vol. 65, no 1, pp. 360–370, 2017, ISSN: 0090-6778.
Resumen | Enlaces | BibTeX | Etiquetas: BCJR, complex-valued, Expectation propagation (EP), ISI, Journal, turbo equalization
@article{Santos2017,
title = {Expectation Propagation as Turbo Equalizer in ISI Channels},
author = {Irene Santos and Juan Jose Murillo-Fuentes and Rafael Boloix-Tortosa and Eva Arias-de-Reyna and Pablo M Olmos},
url = {http://ieeexplore.ieee.org/document/7587428/},
doi = {10.1109/TCOMM.2016.2616141},
issn = {0090-6778},
year = {2017},
date = {2017-01-01},
journal = {IEEE Transactions on Communications},
volume = {65},
number = {1},
pages = {360--370},
abstract = {In probabilistic equalization of channels with inter-symbol interference, the BCJR algorithm and its approximations become intractable for high-order modulations, even for moderate channel dispersions. In this paper, we introduce a novel soft equalizer to approximate the symbol a posteriori probabilities (APP), where the expectation propagation (EP) algorithm is used to provide an accurate estimation. This new soft equalizer is presented as a block solution, denoted as block-EP (BEP), where the structure of the matrices involved is exploited to reduce the complexity order to O(LN2) , i.e., linear in the length of the channel, L , and quadratic in the frame length, N . The solution is presented in complex-valued formulation within a turbo equalization scheme. This algorithm can be cast as a linear minimum-mean-squared-error (LMMSE) turbo equalization with double feedback architecture, where constellations being discrete is a restriction exploited by the EP that provides a first refinement of the APP. In the experiments included, the BEP exhibits a robust performance, regardless of the channel response, with gains in the range 1.5\textendash5 dB compared with the LMMSE equalization.},
keywords = {BCJR, complex-valued, Expectation propagation (EP), ISI, Journal, turbo equalization},
pubstate = {published},
tppubtype = {article}
}
In probabilistic equalization of channels with inter-symbol interference, the BCJR algorithm and its approximations become intractable for high-order modulations, even for moderate channel dispersions. In this paper, we introduce a novel soft equalizer to approximate the symbol a posteriori probabilities (APP), where the expectation propagation (EP) algorithm is used to provide an accurate estimation. This new soft equalizer is presented as a block solution, denoted as block-EP (BEP), where the structure of the matrices involved is exploited to reduce the complexity order to O(LN2) , i.e., linear in the length of the channel, L , and quadratic in the frame length, N . The solution is presented in complex-valued formulation within a turbo equalization scheme. This algorithm can be cast as a linear minimum-mean-squared-error (LMMSE) turbo equalization with double feedback architecture, where constellations being discrete is a restriction exploited by the EP that provides a first refinement of the APP. In the experiments included, the BEP exhibits a robust performance, regardless of the channel response, with gains in the range 1.5–5 dB compared with the LMMSE equalization.
2015
Santos, Irene; Murillo-Fuentes, Juan Jose; Olmos, Pablo M
Block Expectation Propagation Equalization for ISI Channels Proceedings Article
En: 2015 23rd European Signal Processing Conference (EUSIPCO), pp. 379–383, IEEE, Nice, 2015, ISBN: 978-0-9928-6263-3.
Resumen | Enlaces | BibTeX | Etiquetas: Approximation algorithms, Approximation methods, BCJR algorithm, channel equalization, Complexity theory, Decoding, Equalizers, expectation propagation, ISI, low complexity, Signal processing algorithms
@inproceedings{Santos2015,
title = {Block Expectation Propagation Equalization for ISI Channels},
author = {Irene Santos and Juan Jose Murillo-Fuentes and Pablo M Olmos},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7362409},
doi = {10.1109/EUSIPCO.2015.7362409},
isbn = {978-0-9928-6263-3},
year = {2015},
date = {2015-08-01},
booktitle = {2015 23rd European Signal Processing Conference (EUSIPCO)},
pages = {379--383},
publisher = {IEEE},
address = {Nice},
abstract = {Actual communications systems use high-order modulations and channels with memory. However, as the memory of the channels and the order of the constellations grow, optimal equalization such as BCJR algorithm is computationally intractable, as their complexity increases exponentially with the number of taps and size of modulation. In this paper, we propose a novel low-complexity hard and soft output equalizer based on the Expectation Propagation (EP) algorithm that provides high-accuracy posterior probability estimations at the input of the channel decoder with similar computational complexity than the linear MMSE. We experimentally show that this quasi-optimal solution outperforms classical solutions reducing the bit error probability with low complexity when LDPC channel decoding is used, avoiding the curse of dimensionality with channel memory and constellation size.},
keywords = {Approximation algorithms, Approximation methods, BCJR algorithm, channel equalization, Complexity theory, Decoding, Equalizers, expectation propagation, ISI, low complexity, Signal processing algorithms},
pubstate = {published},
tppubtype = {inproceedings}
}
Actual communications systems use high-order modulations and channels with memory. However, as the memory of the channels and the order of the constellations grow, optimal equalization such as BCJR algorithm is computationally intractable, as their complexity increases exponentially with the number of taps and size of modulation. In this paper, we propose a novel low-complexity hard and soft output equalizer based on the Expectation Propagation (EP) algorithm that provides high-accuracy posterior probability estimations at the input of the channel decoder with similar computational complexity than the linear MMSE. We experimentally show that this quasi-optimal solution outperforms classical solutions reducing the bit error probability with low complexity when LDPC channel decoding is used, avoiding the curse of dimensionality with channel memory and constellation size.