We introduce a new approach to reservoir computing (RC) in which single nonlinear device – semiconductor optical amplifier, replaces the entire nonlinear reservoir to perform computations. To study the performance of the proposed scheme, we use it for the benchmark prediction task of learning the Mackey-Glass chaotic attractor. Mildly chaotic attractor with tau = 17 and wilder chaotic behavior with tau = 30 are considered.
We consider a model nondispersive nonlinear optical fiber channel with additive Gaussian noise at large SNR (signal-to-noise ratio) in the intermediate power region. Using Feynman path-integral technique we find the optimal input signal distribution maximizing the channel’s per-sample mutual information. The finding of the optimal input signal distribution allows us to improve previously known estimates for the channel capacity. We show that in the intermediate power regime the per-sample mutual information for the optimal input signal distribution is greater than the per-sample mutual information for the Gaussian and half-Gaussian input signal distributions.
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