Abstract

Brain-inspired intelligence using the cognitive dynamic system (CDS) concept is proposed to control the quality-of-service (QoS) over a long-haul fiber-optic link that is nonlinear and with non-Gaussian channel noise. Digital techniques such as digital-back-propagation (DBP) assume that the fiber optic link parameters, such as loss, dispersion, and nonlinear coefficients, are known at the receiver. However, the proposed CDS does not need to know about the fiber optic link physical parameters, and it can improve the bit error rate (BER) or enhance the data rate based on information extracted from the fiber optic link. The information extraction (Bayesian statistical modeling) using intelligent perception processing on the received data, or using the previously extracted models in the model library, is carried out to estimate the transmitted data in the receiver. Then, the BER is sent to the executive through the main feedback channel and the executive produces actions on the physical system/signal to ensure that the BER is continuously under the forward-error-correction (FEC) threshold. Therefore, the proposed CDS is an intelligent and adaptive system that can mitigate disturbance in the fiber optic link (especially in an optical network) using prediction in the perceptor and/or doing proper actions in the executive based on BER and the internal reward. A simplified CDS was implemented for nonlinear fiber optic systems based on orthogonal frequency division multiplexing (OFDM) to show how the proposed CDS can bring noticeable improvement in the system’s performance. As a result, enhancement of the data rate by 12.5% and the Q-factor improvement of 2.74 dB were achieved in comparison to the conventional system (i.e., the system without smart brain).

Highlights

  • Nowadays, the autonomic decision-making systems (ADMS) [1] for the smart interactive cyber-physical systems are attracting much attention from researchers and technology providers [1,2,3,4,5].In this paper, a cognitive dynamic system (CDS) based on ADMS concept is presented for the fiber optic communication systems

  • 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

  • The block diagram of the CDS consisting of the preceptor, the main feedback channel, and executive is presented

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Summary

Introduction

The autonomic decision-making systems (ADMS) [1] for the smart interactive cyber-physical systems are attracting much attention from researchers and technology providers [1,2,3,4,5]. The proposed CDS can improve the BER and/or enhance the data rate based on the intelligent processing of the received data, which includes the extraction of a statistical model of the fiber optic channel or the use of the previously extracted models in the model library. The proposed CDS is an intelligent and adaptive system that can tackle a disturbance in the fiber optic link (especially in the optical network) using prediction in the perceptor and/or taking proper actions in the executive based on BER and the internal reward. Inspired by neuroscience and the human brain, the PAC is the cybernetic information-processing loop that helps the living organism to adapt dynamically to its environment (e.g., the environment can be the transmission medium in a fiber optic link) by aim-directed behavior or language [6] In these activities, the CDS functions like the human brain and processes the measured information from sensors [7]. This process continues until the cup is close to her mouth (PAC)

Memory
Applications ofand thethe for the sends
Perceptor
Three-Layered Bayesian Generative Model
Layer III
The probability in
Previous Model Selection
Bayesian Equation
Selecting Maximum Probability and BER Calculation
The Executive
Basic Actions
Advanced Actions for BER Improvement
Policy
The CDS with a Simple Executive
The Algorithm of CDS with a Simplified Executive
18: Localize the set of all close actions to ck Learning
Simulation Results in Presence of Disturbance
CDS Complexity
Conclusions
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