Compressed Shaping: Concept and FPGA Demonstration
Journal article, 2021

Probabilistic shaping (PS) has been widely studied and applied to optical fiber communications. The encoder of PS expends the number of bit slots and controls the probability distribution of channel input symbols. Not only studies focused on PS but also most works on optical fiber communications have assumed source uniformity (i.e. equal probability of marks and spaces) so far. On the other hand, the source information is in general nonuniform, unless bit-scrambling or other source coding techniques to balance the bit probability is performed. Interestingly, one can exploit the source nonuniformity to reduce the entropy of the channel input symbols with the PS encoder, which leads to smaller required signal-to-noise ratio at a given input logic rate. This benefit is equivalent to a combination of data compression and PS, and thus we call this technique compressed shaping. In this work, we explain its theoretical background in detail, and verify the concept by both numerical simulation and a field programmable gate array (FPGA) implementation of such a system. In particular, we find that compressed shaping can reduce power consumption in forward error correction decoding by up to 90% in nonuniform source cases. The additional hardware resources required for compressed shaping are not significant compared with forward error correction coding, and an error insertion test is successfully demonstrated with the FPGA.

probabilistic shaping

modulation

Coding

entropy

optical fiber communication

distribution matching

source coding

data compression

implementation

Author

Tsuyoshi Yoshida

Osaka University

Koji Igarashi

Osaka University

Magnus Karlsson

Chalmers, Microtechnology and Nanoscience (MC2), Photonics

Erik Agrell

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks

Journal of Lightwave Technology

0733-8724 (ISSN) 1558-2213 (eISSN)

Vol. 39 17 5412-5422 21082436

Subject Categories

Telecommunications

Signal Processing

Other Electrical Engineering, Electronic Engineering, Information Engineering

DOI

10.1109/JLT.2021.3085974

More information

Latest update

1/11/2022