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Equalization - short reach, MLstructures, ML for OFCS, MLs - Coggle Diagram

- - - - Interaction of CD and dispersion compensation (DC) causing power-fading effects and introducing frequency notches.
      - Complexity of CD equalization in IM/DD systems due to square law detection.
      - Low-pass filtering effects leading to severe inter-symbol interference.
      - Performance degradation caused by non-idealities in low-cost components.
    - - level-dependent skew and noise, produced by lasers, modulators, photodiodes, and amplifiers
- - - - Conversion of the input signal into a 1-D input array with N elements, comprising past and post symbols, in CNN-based nonlinear compensation.
      - Implementation of multiple convolutional layers and fully-connected layers with a nonlinear activation function in CNN-based equalizers.
    - - Experimental demonstrations showing considerable performance improvement compared to a DNN-based approach.
  - - - Use the feedback of past output values as an additional input while calculating the present output value
        Ref1: Recurrent neural network (RNN) based end-to-end nonlinear managementfor symmetrical 50Gbps NRZPONwith29dB+lossbudget
        Ref2:Feedforward and re current neural network-based transfer learning for nonlinear equalization in short-reach optical links
      - Auto-regressive RNN and layer RNN are two common types of RNNs, with auto-regressive RNN showing better equalization performance.
    - - An RNN-based equalizer with parallel outputs was investigated for a 100 Gb/s passive optical network application using FPGA implementation.
        Ref: Recurrent neural network based equalizer with embedded parallelization for 100Gbps PON
      - LSTMs (Long Short-Term Memory networks), a variant of RNNs, were also demonstrated for equalization in a 50 Gb/s PAM-4 transmission system.
        Ref: LSTM networks enabled nonlinear equalization in 50-Gb/s PAM-4 transmission links
      - RNNs outperform FFNNs analogous to the performance improvement observed with the combination of FFE and DFE compared to FFE alone.
        Ref: Computational complexity comparison of feedforward/radial basis function/recurrent neu ral network-based equalizer for a 50-Gb/s PAM4 direct-detection optical link
  - - - A sufficiently large NN with at least one hidden layer can suitable for equalization tasks.
      - The input vector of the equalizer corresponds to consecutive sampled symbols, with the length of the vector considering channel memory.
      - NNs can be structured with a single hidden layer and a large number of nodes or multiple hidden layers (i.e., a DNN) with relatively fewer nodes.
    - - Choosing the nonlinear activation function in each hidden layer is crucial for approximating nonlinear functions to address distortion in short-reach systems. Common activation functions: sigmoid, rectified linear unit (ReLU), and hyperbolic tangent (tanh) function.
      - The Softmax activation function is typically chosen for the output layer to facilitate symbol decisions for any PAM-N signal in addition to equalization.
    - - Experimental demonstrations have shown that NN-based equalizers outperform conventional equalizers like FFE and Volterra nonlinear equalizers.
        Ref1: Machine learning for 100Gb/s/ passive optical network.
        Ref2: Artificial neural networks for linear and non-linear im pairment mitigation in high-baudrate IM/DD systems
    - - Additionally, an FPGA implementation of a fixed-point DNN-based equalizer has been demonstrated for high-speed passive optical networks.
        Ref:FPGA implementation of deep neural network based equalizers for high-speed PON