Initialized autoencoders to find constellations robust to residual laser phase noise

Abstract

Autoencoder (AE) has been used to find quadrature amplitude modulation (QAM) constellations robust to optical impairments, such as residual laser phase noise or fiber nonlinearities. We examine AE for constellations for fiber links with residual phase noise following phase recovery. We study 1) improved constellations via AE neural network (NN) initialization, 2) bit-to-symbol mappings for AE constellations, and 3) performance enhancement from the AE decoder as a detection algorithm. We compare our performance to several other phase-noise-optimized constellations, using an identical phase recovery method for fair comparison. We confirm the improved performance of our constellations experimentally as well as via simulation. Our AE initialization technique (using previous constellations found experimentally, empirically, numerically, etc.) is shown to improve constellations for 16QAM and 64QAM. A bit-to-symbol mapping is essential to benefit fully from AE constellations, but is intractable to optimize. We use an ad hoc approach that derives mappings from our initialization constellations. Our AE initialization technique leads to performance improvements over the previous QAM constellations under all metrics: generalized mutual information (GMI), bit error rate (BER) and symbol error rate (SER). We examine the gap between BER and SER due to non-optimal bit-to-symbol mappings. The AE approach to constellation design has as a byproduct an optimized detector: the AE decoder. We quantify the improvement of this detector over the classic maximum likelihood detection that neglects phase noise.