Probabilistically shaped signaling and machine learning detection for optical interconnection

Abstract

We reviewed our recent progresses regarding probabilistic shaping for signaling and machine-learning for detection in short reach optical interconnection systems, regarding VCSEL based MMF link and silicon MRM based SMF link. Software defined optical interconnection of optical signaling and adaptive detection have been realized. Firstly, probabilistic shaping (PS) for PAM-4 and PAM-8 signaling over vertical cavity surface emitting laser (VCSEL) has been proposed and demonstrated experimentally for short-reach optical interconnection applications. With the use of prefix-free dyadic matcher, the proposed PS-PAM signals have shown improved signal-to-noise ratio (SNR) tolerance and energy efficiency. To evaluate the performance of dyadic PMF shaping, theoretical comparisons of achievable information rates (AIRs) between uniformly-distributed PAM-N and PS-PAM-N are carried out under the constrained signal-to-ratios (SNRs) and channel bandwidths condition. 0.18-bit AIR improvement of PAM-8 is achieved by using dyadic shaping, at SNR of 7 dB. The experiment verifications are presented on VCSEL&OM3 fiber links with 16.6-GHz 10-dB back-to-bck bandwidth. Secondly, we employ a machine learning algorithm for detection of PAM-4 modulated signals. Our approach is based on the support vector machine (SVM) method and we applied it to mitigate the distortion of a silicon micro-ring modulator (Si-MRM). We characterize the nonlinearity distortion in terms of level deviation (LD) of PAM-4 arising from wavelength drift. Up to 2.7-dB receiver sensitivity gain is obtained at about 26% LD by using the proposed SVM machine learning method. The receiver sensitivity-float range can be squeezed to be within 0.3 dB even with up to 30% LD. Up to 3.63-dB receiver sensitivity improvement has been achieved at 50 Gbps for a Si-MRM after 2-km standard single mode fiber (SSMF) transmission.