Due to increasingly large computational resources, modern neural networks are severely constrained due to their processing speed and energy consumption. Optical neural networks (ONNs), which use photonic structures to process signals at the physical level as an alternative to the computation in the electronic domain provided by traditional neural networks, are an attractive approach to implementing ultra-high-speed, low-energy parallel computation. Nevertheless, current training processes for electronic domain neural networks are optimized from gradient-based training methods, such as backpropagation, not compatible with ONNs with gradient-free features. In this work, a stochastic function-based gradient-free training method, i.e., stochastic function direct feedback alignment (SF-DFA) is demonstrated and evaluated. SF-DFA trains a gradient-free system using stochastic matrices and functions to replace the weights and gradients of the nodes in neural networks. Thus, it is feasible to train ONNs without a prior knowledge of the photonic system and its gradients. In addition, implementing such training process on optical hardware is also known to be possible. A series of studies have been carried out for a spectral slicing neural network (SS-NN) architecture trained by SF-DFA. The SS-NN system uses bandpass filters embedded in optical fiber micro rings to enable slicing of the optical signal spectrum. Our results demonstrate that the training of ONN using SF-DFA can converge efficiently, with higher processing speed and lower energy consumption compared to back-propagation.
KEYWORDS: Telecommunications, Signal to noise ratio, Fiber optic communications, Optical amplifiers, Digital signal processing, Fiber amplifiers, Modulation, Binary data, Systems modeling
Due to the high transmission capacity, optical fiber systems have been widely applied in the modern telecommunication infrastructure to meet the ever-increasing demand of data traffic. Optical amplifiers have been employed to amplify optical signals and to compensate for the transmission losses. They play a key role in relaying the signals in ultra-wideband optical fiber communication systems. However, the amplified spontaneous emission (ASE) noise will be introduced and will pose constraints on the transmission information rates. The mutual information (MI) and the generalized mutual information (GMI) have been applied to evaluate the information rates in communication systems. In this work, we have investigated the impact of ASE noise on the MI and the GMI, and developed corresponding analyses across different modulation formats. Our work aims to explore the limit and requirements on optical amplifiers in next-generation ultra-wideband optical fiber communication systems.
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