Deep Learning-Assisted High-Pass-Filter-Based Fixed-Threshold Decision for Free-Space Optical Communications

Abstract

This paper proposes a deep learning (DL)-assisted high-pass-filter (HPF)-based fixed-threshold decision (FTD) for free-space optical (FSO) communication. HPF is applied to reduce the scintillation effect by filtering out the low-frequency components of the received signal. However, the performance is limited owing to the signal distortion from HPF and remnant scintillation effect due to insufficient filtering. Therefore, the DL model is adopted to improve the performance of HPF-based scintillation effect compensation. The multilayer perceptron (MLP) model is used to adaptively select the peak frequency component of the received signal as the optimized cutoff frequency of HPF. Furthermore, recurrent neural network (RNN) and long short-term memory (LSTM) models are cascaded after HPF to compensate for the remnant scintillation effect and recover the signal distortion without the optimization of HPF cutoff frequency. The simulation was conducted under different turbulence channels and data rates. Simulation results showed that MLP-assisted adaptive optimized cutoff frequency and cascaded LSTM and HPF methods were close to the adaptive-threshold decision with precise channel state information under various turbulence channel degrees.