Coherent Optical Communications Enhanced by Machine Intelligence
This work addresses uncertainty in signal discrimination for optical communications, potentially enhancing both classical and quantum technologies, though it appears incremental as it builds on existing methods with machine learning integration.
The paper tackled the problem of discriminating weak coherent signals in free-space optical communications by designing a scheme using balanced homodyne detection with an unsupervised generative machine learning and CNN system, achieving the classical optimal limit and significantly reducing error probability.
Uncertainty in discriminating between different received coherent signals is integral to the operation of many free-space optical communications protocols, and is often difficult when the receiver measures a weak signal. Here we design an optical communications scheme that uses balanced homodyne detection in combination with an unsupervised generative machine learning and convolutional neural network (CNN) system, and demonstrate its efficacy in a realistic simulated coherent quadrature phase shift keyed (QPSK) communications system. Additionally, we program the neural network system at the transmitter such that it autonomously learns to correct for the noise associated with a weak QPSK signal, which is shared with the network state of the receiver prior to the implementation of the communications. We find that the scheme significantly reduces the overall error probability of the communications system, achieving the classical optimal limit. This communications design is straightforward to build, implement, and scale. We anticipate that these results will allow for a significant enhancement of current classical and quantum coherent optical communications technologies.