Iterative Decision Feedback Equalization Using Online Prediction
In this article, a new category of soft-input soft-output (SISO) minimum-mean square error (MMSE) finite-impulse response (FIR) decision feedback equalizers (DFEs) with iteration-wise static filters (i.e. iteration variant) is investigated. It has been recently shown that SISO MMSE DFE with dynamic...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8974223/ |
| _version_ | 1818621703406747648 |
|---|---|
| author | Serdar Sahin Antonio Maria Cipriano Charly Poulliat Marie-Laure Boucheret |
| author_facet | Serdar Sahin Antonio Maria Cipriano Charly Poulliat Marie-Laure Boucheret |
| author_sort | Serdar Sahin |
| collection | DOAJ |
| description | In this article, a new category of soft-input soft-output (SISO) minimum-mean square error (MMSE) finite-impulse response (FIR) decision feedback equalizers (DFEs) with iteration-wise static filters (i.e. iteration variant) is investigated. It has been recently shown that SISO MMSE DFE with dynamic filters (i.e. time-varying) reaches very attractive operating points for high-data rate applications, when compared to alternative turbo-equalizers of the same category, thanks to sequential estimation of data symbols. However the dependence of filters on the feedback incurs high amount of latency and computational costs, hence SISO MMSE DFEs with static filters provide an attractive alternative for computational complexity-performance trade-off. However, the latter category of receivers faces a fundamental design issue on the estimation of the decision feedback reliability for filter computation. To address this issue, a novel approach to decision feedback reliability estimation through online prediction is proposed and applied for SISO FIR DFE with either a posteriori probability (APP) or expectation propagation (EP) based soft feedback. This novel method for filter computation is shown to improve detection performance compared to previously known alternative methods, and finite-length and asymptotic analysis show that DFE with static filters still remains well-suited for high-spectral efficiency applications. |
| first_indexed | 2024-12-16T18:13:29Z |
| format | Article |
| id | doaj.art-19aea8ec7b154ded940f4a8fb53d8601 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T18:13:29Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-19aea8ec7b154ded940f4a8fb53d86012022-12-21T22:21:43ZengIEEEIEEE Access2169-35362020-01-018236382364910.1109/ACCESS.2020.29703408974223Iterative Decision Feedback Equalization Using Online PredictionSerdar Sahin0https://orcid.org/0000-0002-9359-376XAntonio Maria Cipriano1Charly Poulliat2https://orcid.org/0000-0001-6407-8841Marie-Laure Boucheret3Thales, Gennevilliers, FranceThales, Gennevilliers, FranceIRIT–ENSEEIHT, Toulouse INP (INPT), CNRS, Toulouse, FranceIRIT–ENSEEIHT, Toulouse INP (INPT), CNRS, Toulouse, FranceIn this article, a new category of soft-input soft-output (SISO) minimum-mean square error (MMSE) finite-impulse response (FIR) decision feedback equalizers (DFEs) with iteration-wise static filters (i.e. iteration variant) is investigated. It has been recently shown that SISO MMSE DFE with dynamic filters (i.e. time-varying) reaches very attractive operating points for high-data rate applications, when compared to alternative turbo-equalizers of the same category, thanks to sequential estimation of data symbols. However the dependence of filters on the feedback incurs high amount of latency and computational costs, hence SISO MMSE DFEs with static filters provide an attractive alternative for computational complexity-performance trade-off. However, the latter category of receivers faces a fundamental design issue on the estimation of the decision feedback reliability for filter computation. To address this issue, a novel approach to decision feedback reliability estimation through online prediction is proposed and applied for SISO FIR DFE with either a posteriori probability (APP) or expectation propagation (EP) based soft feedback. This novel method for filter computation is shown to improve detection performance compared to previously known alternative methods, and finite-length and asymptotic analysis show that DFE with static filters still remains well-suited for high-spectral efficiency applications.https://ieeexplore.ieee.org/document/8974223/Decision feedback equalizersinter-symbol interferenceexpectation propagationsemi-analytical receiver abstractionperformance predictionturbo equalization |
| spellingShingle | Serdar Sahin Antonio Maria Cipriano Charly Poulliat Marie-Laure Boucheret Iterative Decision Feedback Equalization Using Online Prediction IEEE Access Decision feedback equalizers inter-symbol interference expectation propagation semi-analytical receiver abstraction performance prediction turbo equalization |
| title | Iterative Decision Feedback Equalization Using Online Prediction |
| title_full | Iterative Decision Feedback Equalization Using Online Prediction |
| title_fullStr | Iterative Decision Feedback Equalization Using Online Prediction |
| title_full_unstemmed | Iterative Decision Feedback Equalization Using Online Prediction |
| title_short | Iterative Decision Feedback Equalization Using Online Prediction |
| title_sort | iterative decision feedback equalization using online prediction |
| topic | Decision feedback equalizers inter-symbol interference expectation propagation semi-analytical receiver abstraction performance prediction turbo equalization |
| url | https://ieeexplore.ieee.org/document/8974223/ |
| work_keys_str_mv | AT serdarsahin iterativedecisionfeedbackequalizationusingonlineprediction AT antoniomariacipriano iterativedecisionfeedbackequalizationusingonlineprediction AT charlypoulliat iterativedecisionfeedbackequalizationusingonlineprediction AT marielaureboucheret iterativedecisionfeedbackequalizationusingonlineprediction |