A Signal Detection Method Based on BiGRU for FSO Communications with Atmospheric Turbulence

In free space optical (FSO) communications, signals are affected by turbulence when transmitted through the atmosphere. Fluctuations in intensity caused by atmospheric turbulence lead to an increase in the bit error rate of FSO systems. Deep learning (DL), as a current research hotspot, offers a promising approach to improve the accuracy of signal detection. In this paper, we propose a signal detection method based on a bidirectional gated recurrent unit (BiGRU) neural network for FSO communications. The proposed detection method considers the temporal correlation of received signals due to the properties of the BiGRU neural network, which is not available in existing detection methods based on DL. In addition, the proposed detection method does not require channel state information (CSI) for channel estimation, unlike maximum likelihood (ML) detection technology with perfect CSI. Numerical results demonstrate that the proposed BiGRU-based detector achieves significant improvements in bit error rate (BER) performance compared with a multilayer perceptron (MLP)-based detector. Specifically, under weak turbulence conditions, the BiGRU-based detector achieves an approximate 2 dB signal-to-noise ratio (SNR) gain at a target BER of 10−6 compared to the MLP-based detector. Under moderate turbulence conditions, it achieves an approximate 6 dB SNR gain at the same target BER of 10−6. Under strong turbulence conditions, the proposed detector obtains a 6 dB SNR gain at a target BER of 10−4. Additionally, it outperforms conventional methods by more than one order of magnitude in BER under the same turbulence and SNR conditions.