Optical Communications with Relative Intensity Noise: Channel Modeling and Information Rates

TL;DR

This paper models optical communication channels affected by laser RIN, revealing signal-dependent noise with memory and analyzing achievable information rates with practical decoding approaches.

Contribution

It introduces a discrete-time channel model with polynomial signal-dependent noise due to RIN, differing from previous models, and evaluates information rates under realistic conditions.

Findings

GMI saturates with increasing constellation size when channel memory is ignored.

Saturation is caused by nonsymmetric contributions of symbols to GMI.

Dense constellations do not improve performance under the studied conditions.

Abstract

We consider optical communications with intensity modulation and direct detection affected by laser relative intensity noise (RIN). Starting from a continuous-time waveform model, we derive an equivalent discrete-time channel model. As a result of RIN, the resulting channel model exhibits signal-dependent noise with memory. Unlike the commonly-assumed model in the literature, the conditional variance of this noise term has a polynomial dependence on the symbol of interest. Finally, we study achievable information rates for this channel under practically-relevant system parameters. We take a mismatched decoding approach and compute the generalized mutual information (GMI) using a memoryless decoding metric. Our numerical results show that when the memory in the channel is ignored by the receiver, GMI saturates as the constellation size increases, and thus, dense constellations do not offer gains. We also show that this saturation results from nonsymmetric nonvanishing contributions of the symbols to the GMI.