Deep-Learning Based Detectors for SISO and SIMO IM/DD FSO Systems

Free space optical (FSO) communication has the potential to develop ultra-fast data links that can be used in a vari-ety of sixth-generation (6G) applications, including heterogeneous networks with enormous connectivity and wireless backhauls for cellular systems. This paper proposes several low-complexity deep neural networks designed to detect received symbols in FSO systems, eliminating the need for channel state information (CSI). We consider different intensity modulation (IM) schemes (e.g., on-off shift keying (OOK) and pulse amplitude modulation (PAM)) and different FSO systems (e.g., single-input single-output (SISO) and single-input multiple-output (SIMO)). We design the neural network to be able to exploit the temporal correlation of the channels and the common information received at every single photodetector (the case of SIMO). The convolutional neural network (CNN) is designed in a way that it can learn the channels without using pilots and also can jointly combine the received signals and detect the symbols for a wide range of signal-to-noise ratio (SNR) values and different turbulence levels. Numerical results show that the proposed CNN demonstrates a better performance compared to the maximum likelihood approach that utilizes imperfect channel information for symbol detection.