An improved deep learning-based end-to-end autoencoder for UAV-to-ground free space optical communication

Abstract

In this paper, an improved deep learning-based end-to-end autoencoder is proposed for unmanned aerial vehicle (UAV) to ground free space optical communication to mitigate atmospheric turbulence. Deep neural network (DNN) is applied to the intensity modulation/direct detection (IM/DD) autoencoder, including transmitter, receiver as well as channel model. The performance is improved by two-stage deep learning training because the minimum Hamming distance between the codewords is increased through pre-training. Simulation results show that the bit error rate of our proposed scheme can reach the 7% hard-decision forward error correction (HD-FEC) threshold at signal-to-noise ratio of approximately 22 dB and in strong atmospheric turbulence where the maximum Rytov variance is 3.5. Our proposed scheme can outperform the state-of-the-art IM/DD system with PPM transmitter and maximum likelihood receiver by achieving approximately 12 dB improvement and reducing ∼51.3% of the decoder's running time without the need for accurate channel state information.