Effective input noise temperature

Algorithm

Control (management)

Engineering

Bandwidth (computing)

Noise figure

Gaussian noise

Quadrature amplitude modulation

Telecommunications

Amplifier

Computer science

Phase noise

Image (mathematics)

Bit error rate

Electronic engineering

Noise floor

Artificial intelligence

Noise measurement

Transmission (telecommunications)

Channel (broadcasting)

Control theory (sociology)

Noise reduction

Noise (video)

CMOS

Journal of Lightwave Technology

Vendor interoperability is one of the desired future characteristics of optical networks. This means that the transmission system needs to support a variety of hardware with different components, leading to system uncertainties throughout the network. For example, uncertainties in signal-to-noise ratio and laser linewidth can negatively affect the quality of transmission within an optical network due to e.g. mis-parametrization of the transceiver signal processing algorithms. In this paper, we propose to geometrically optimize a constellation shape that is robust to uncertainties in the channel conditions by utilizing end-to-end learning. In the optimization step, the channel model includes additive noise and residual phase noise. In the testing step, the channel model consists of laser phase noise, additive noise and blind phase search as the carrier phase recovery algorithm. Two noise models are considered for the additive noise: white Gaussian noise and nonlinear interference noise model for fiber nonlinearities. The latter models the behavior of an optical fiber channel more accurately because it considers the nonlinear effects of the optical fiber. For this model, the uncertainty in the signal-to-noise ratio can be divided between amplifier noise figures and launch power variations. For both noise models, our results indicate that the learned constellations are more robust to the uncertainties in channel conditions compared to a standard constellation scheme such as quadrature amplitude modulation and standard geometric constellation shaping techniques.

End-to-End Learning of a Constellation Shape Robust to Channel Condition Uncertainties