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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. ; Monteiro, P.

Optics Express Vol. 30, Nº 23, pp. 41459 - 41459, October, 2022.

ISSN (print): 1094-4087
ISSN (online):

Scimago Journal Ranking: 1,23 (in 2021)

Digital Object Identifier: 10.1364/OE.468836

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Abstract
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.