Abstract
Advanced nonlinear digital signal processing technologies, which bring significant performance gain for high-speed optical interconnects, are highly constrained by huge complexity in the actual deployment. Fully connected neural network-based equalizer has shown powerful efficacy to deal with the complex linear and nonlinear impairments for VCSEL-enabled multi-mode optical interconnects, but it also contains a number of redundancies with little impact on performance improvement. In this article, we experimentally demonstrate a compressed neural network equalization using the iterative pruning algorithm for 112-Gbps VCSEL-enabled PAM-4 and PAM-8 transmissions over 100-m MMF. We also study the impact of threshold and pruning span on the performance of proposed algorithms. The results show up to 71% connection compression by use of the iterative pruning algorithms and maximum 28.4% improvement compared with the one-shot pruning algorithm.
PDF Article
More Like This
Cited By
You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.
Contact your librarian or system administrator
or
Login to access Optica Member Subscription