End-to-end deep learning of geometric shaping for unamplified coherent systems
Oliveira, B. M.
Neves, M. S.
Guiomar, F. P.
Medeiros, M. C. R.
Optics Express Vol. 30, Nº 23, pp. 41459 - 41459, October, 2022.
ISSN (print): 1094-4087
Scimago Journal Ranking: 1,23 (in 2021)
Digital Object Identifier: 10.1364/OE.468836
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With the increasing data rate requirements on short-reach links, the recent standardization of unamplified coherent optical systems is paving the way for a cost and power-effective solution, targeting a massive deployment in the near future. However, unamplified systems are introducing new challenges. Particularly, the performance is highly dependent on the peak-to-average power ratio (PAPR) of the transmitted signal, which puts at question the use of the typical constellation formats. In this work, we use an end-to-end deep learning framework to optimize the geometry of different constellation sizes, ranging from 8- to 128-ary constellations. In general, it is shown that the performance of these systems is maximized with constellations whose outer symbols are disposed in a square shape, owing to the minimization of the real-valued PAPR. Following this premise, we experimentally demonstrate that odd-bit constellations can be significantly optimized for unamplified coherent links, achieving power budget gains in the range of 0.5–3 dB through the geometric optimization of 8-, 32- and 128-ary constellations.