Naghshvarianjahromi Mahdi, Kumar Shiva, Deen M Jamal
Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON L8S4K1, Canada.
Sensors (Basel). 2019 May 10;19(9):2175. doi: 10.3390/s19092175.
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).
提出了一种利用认知动态系统（CDS）概念的脑启发式智能，用于控制长距离光纤链路的服务质量（QoS），该链路具有非线性且存在非高斯信道噪声。诸如数字反向传播（DBP）之类的数字技术假定光纤链路参数，如损耗、色散和非线性系数，在接收器端是已知的。然而，所提出的CDS不需要了解光纤链路的物理参数，并且它可以根据从光纤链路提取的信息来提高误码率（BER）或提高数据速率。通过对接收数据进行智能感知处理，或使用模型库中先前提取的模型来进行信息提取（贝叶斯统计建模），以在接收器中估计传输的数据。然后，BER通过主反馈通道发送到执行器，执行器对物理系统/信号采取行动，以确保BER持续低于前向纠错（FEC）阈值。因此，所提出的CDS是一个智能自适应系统，它可以通过感知器中的预测和/或基于BER和内部奖励在执行器中采取适当行动来减轻光纤链路（特别是在光网络中）的干扰。基于正交频分复用（OFDM）为非线性光纤系统实现了一个简化的CDS，以展示所提出的CDS如何能在系统性能上带来显著改善。结果，与传统系统（即没有智能大脑的系统）相比，实现了数据速率提高12.5%以及Q因子提高2.74 dB。
