Abstract
In this paper, we apply deep learning for communication over dispersive channels with power detection, as encountered in low-cost optical intensity modulation/direct detection (IMDD) links. We consider an autoencoder based on the recently proposed sliding window bidirectional recurrent neural network (SBRNN) design to realize the transceiver for optical IMDD communication. We show that its performance can be improved by introducing a weighted sequence estimation scheme at the receiver. Moreover, we perform bit-to-symbol mapping optimization to reduce the bit-error rate (BER) of the system. Furthermore, we carry out a detailed comparison with classical schemes based on pulse-amplitude modulation and maximum likelihood sequence detection (MLSD). Our investigation shows that for a reference 42 Gb/s transmission, the SBRNN autoencoder achieves a BER performance comparable to MLSD, when both systems account for the same amount of memory. In contrast to MLSD, the SBRNN performance is achieved without incurring a computational complexity exponentially growing with the processed memory.
Original language | English |
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Title of host publication | 2019 57th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2019 |
Place of Publication | Piscataway |
Publisher | Institute of Electrical and Electronics Engineers |
Pages | 192-199 |
Number of pages | 8 |
ISBN (Electronic) | 9781728131511 |
DOIs | |
Publication status | Published - Sept 2019 |
Event | 57th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2019 - Monticello, United States Duration: 24 Sept 2019 → 27 Sept 2019 |
Conference
Conference | 57th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2019 |
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Country/Territory | United States |
City | Monticello |
Period | 24/09/19 → 27/09/19 |
Funding
The work received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie project COIN (grant agreement No. 676448). G. Liga gratefully acknowledges the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No757791)