Geometric Shaping of 2-D Constellations in the Presence of Laser Phase Noise
TLDR
In this paper, a geometric shaping (GS) strategy was proposed to design 8, 16, 32, and 64 -ary modulation formats for the optical fiber channel impaired by both additive white Gaussian (AWGN) and phase noise.Abstract:
In this article, we propose a geometric shaping (GS) strategy to design 8, 16, 32, and 64 -ary modulation formats for the optical fibre channel impaired by both additive white Gaussian (AWGN) and phase noise. The constellations were optimised to maximise generalised mutual information (GMI) using a mismatched channel model. The presented formats demonstrate an enhanced signal-to-noise ratio (SNR) tolerance in high phase noise regimes when compared with their quadrature amplitude modulation (QAM) or AWGN-optimised counterparts. By putting the optimisation results in the context of the 400ZR implementation agreement, we show that GS alone can either relax the laser linewidth (LW) or carrier phase estimation (CPE) requirements of 400 Gbit/s transmission links and beyond. Following the GMI validation, the performance of the presented formats was examined in terms of post forward error correction (FEC) bit-error-rate (BER) for a soft decision (SD) extended Hamming code (128, 120), implemented as per the 400ZR implementation agreement. We demonstrate gains of up to 1.2 dB when compared to the 64 -ary AWGN shaped formats.read more
Citations
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Posted Content
End-to-end Learning of a Constellation Shape Robust to Channel Condition Uncertainties
TL;DR: In this article, the authors proposed to geometrically optimize a constellation shape that is robust to uncertainties in the channel conditions by utilizing end-to-end learning, and the results indicate that the learned constellations are more robust to uncertainty in channel conditions compared to a standard constellation scheme such as quadrature amplitude modulation and more importantly standard geometric constellation shaping techniques.
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