Comparison of Photonic Reservoir Computing Systems for Fiber Transmission Equalization

In recent years, various methods, architectures, and implementations have been proposed to realize hardware-based reservoir computing (RC) for a range of classification and prediction tasks. Here we compare two photonic platforms that owe their computational nonlinearity to an optically injected semiconductor laser and to the optical transmission function of a Mach-Zehnder modulator, respectively. We numerically compare these platforms in a delay-based reservoir computing framework, in particular exploring their ability to perform equalization tasks on nonlinearly distorted signals at the output of a fiber-optic transmission line. Although the non-linear processing provided by the two systems is different, both produce a substantial reduction of the bit-error-rate (BER) for such signals of up to several orders of magnitude. We show that the obtained equalization performance depends significantly on the operating conditions of the physical systems, the size of the reservoir and the output layer training method.