Knowledge Distillation Applied to Optical Channel Equalization: Solving the Parallelization Problem of Recurrent Connection

Moreover, with the ever-increasing transmission bandwidth, nonlinearity becomes even more important [1]. Various digital signal processing (DSP) techniques have been proposed to minimize nonlinear effects [2]. Due to the universal approximation capability of neural networks (NNs), the NNs have recently been intensively studied for the optical channel post-equalization, because they can approximate the inverse optical channel transfer function with good accuracy and revert the nonlinear distortions. In particular, recurrent NNs (RNN) based equalizers have shown the best capability in equalizing nonlinear impairments as compared to the feed-forward NN types [3-5]. However, since the RNN structure has a feedback loop, it is not easily parallelizable.