Stochastic Digital Backpropagation with Residual Memory Compensation
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2015
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| _version_ | 1866911763237175296 |
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| author | Irukulapati, Naga V. Marsella, Domenico Johannisson, Pontus Agrell, Erik Secondini, Marco Wymeersch, Henk |
| author_facet | Irukulapati, Naga V. Marsella, Domenico Johannisson, Pontus Agrell, Erik Secondini, Marco Wymeersch, Henk |
| contents | Stochastic digital backpropagation (SDBP) is an extension of digital backpropagation (DBP) and is based on the maximum a posteriori principle. SDBP takes into account noise from the optical amplifiers in addition to handling deterministic linear and nonlinear impairments. The decisions in SDBP are taken on a symbol-by-symbol (SBS) basis, ignoring any residual memory, which may be present due to non-optimal processing in SDBP. In this paper, we extend SDBP to account for memory between symbols. In particular, two different methods are proposed: a Viterbi algorithm (VA) and a decision directed approach. Symbol error rate (SER) for memory-based SDBP is significantly lower than the previously proposed SBS-SDBP. For inline dispersion-managed links, the VA-SDBP has up to 10 and 14 times lower SER than DBP for QPSK and 16-QAM, respectively. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1506_02937 |
| institution | arXiv |
| publishDate | 2015 |
| record_format | arxiv |
| spellingShingle | Stochastic Digital Backpropagation with Residual Memory Compensation Irukulapati, Naga V. Marsella, Domenico Johannisson, Pontus Agrell, Erik Secondini, Marco Wymeersch, Henk Information Theory Optics Stochastic digital backpropagation (SDBP) is an extension of digital backpropagation (DBP) and is based on the maximum a posteriori principle. SDBP takes into account noise from the optical amplifiers in addition to handling deterministic linear and nonlinear impairments. The decisions in SDBP are taken on a symbol-by-symbol (SBS) basis, ignoring any residual memory, which may be present due to non-optimal processing in SDBP. In this paper, we extend SDBP to account for memory between symbols. In particular, two different methods are proposed: a Viterbi algorithm (VA) and a decision directed approach. Symbol error rate (SER) for memory-based SDBP is significantly lower than the previously proposed SBS-SDBP. For inline dispersion-managed links, the VA-SDBP has up to 10 and 14 times lower SER than DBP for QPSK and 16-QAM, respectively. |
| title | Stochastic Digital Backpropagation with Residual Memory Compensation |
| topic | Information Theory Optics |
| url | https://arxiv.org/abs/1506.02937 |