Brain Inspired Dynamic System for the Quality of Service Control over the Long-Haul Nonlinear Fiber-Optic Link  
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of AI, it would be the parent that puts the child's hand on the hot pot for punishment (rewards from the environment). Therefore, for bringing intelligence to fiber optic communications, we choose CDS instead of AI. CDS can be used as the brain of fiber optic transceiver. The capacity and reach of a fiber optic system are mainly limited by fiber nonlinear effects [11; 12]. Some techniques have been proposed to mitigate fiber nonlinear impairments. They can be classified as (i) optical techniques, such as optical phase conjugation [13; 14] and optical backpropagation [15; 16; 17; 18]; (ii) optoelectronic techniques, such as compensation using a phase modulator [19]; and (iii) digital techniques, such as digital backpropagation (DBP) [20; 21], perturbation techniques [22; 23], nonlinear Fourier transform [24; 25; 26], and Volterra series approach [27; 28; 29]. The optical/optoelectronic techniques require additional photonic/optoelectronic components, while the digital techniques, such as DBP, require computational architecture with higher cost. In addition, these techniques require the fiber optic system parameters, such as the number of fiber spans, span length, dispersion, and nonlinear coefficients of each fiber span. In addition, each time a data sequence is sent, extensive signal processing is done by these techniques. The signal processing is data dependent due to the nonlinear nature of the system. Although there have been recent efforts to make the DBP adaptive [30; 31], the computational cost is high. As a result, we need a technique that is adaptive, intelligent, and works without any previous knowledge about the photonics/optoelectronic components in the network. It should have a low cost and be easy to install. In addition, it should adapt itself to any fluctuations in the network such as power fluctuation or link length variations. The proposed brain-inspired dynamic system is designed to meet these requirements. In this paper, for the first time to our knowledge, the principles of CDS are applied to a nonlinear fiber optic communication system for the bit error rate (BER) improvement and the data-rate enhancement. Some of the subsystems of the CDS, such as probabilistic shaping and Bayesian filtering, could interact coherently and systematically to improve the BER and the data-rates. The typical CDS has three main subsystems: (i) perceptor, (ii) main feedback channel, and (iii) executive. The main contribution of this paper is the design of the perceptor and executive for a long-haul nonlinear fiber optic system. The executive, the preceptor, and the feedback channel are placed in the receiver (Rx), and the executive sends action, such as new data rate by the low data rate link, to the transmitter (Tx). The basic principles of CDS are discussed in detail in [7]. The perceptor proposed here has some similarities and differences to that proposed in [7]. The typical perceptor is made up of three main subsystems: (i) Bayesian modeling, (ii) Bayesian filtering, and (iii) entropic state [7]. The perceptor presented here for fiber optic applications has some new features. The perceptor operates in two modes: (i) the prediction mode and (ii) the BER improvement mode. If the BER is less than the FEC threshold, the CDS extracts a statistical model of the fiber optic channel and saves it in the model library. Under this condition, the CDS operates in the BER improvement mode. If there is a disturbance in the fiber optic channel, the BER could increase and exceed the forward error correction (FEC) threshold. As a result, the CDS switches to the prediction mode in which the preceptor selects one of the models in the model library that is closest to the current condition.