Free space optical (FSO) communication technology has become increasingly advanced with capabilities of high speed, high capacity, and low power consumption. However, despite the great potential of FSO, its performance is limited in a turbulent atmosphere. Atmospheric turbulence causes scintillation in the FSO propagated signals, leading to an increase in the bit error rate (BER) performance of the recovered signals at the receiver. In this paper, we demonstrate that the use of deep learning (DL) detection methods could overcome these limitations. We present a new detection method of on-off keying (OOK) modulated signals by using different models of DL over different strength FSO turbulent channels, without the need for prior knowledge of the parameters of the channel. The demonstrated DL decoders improve the performance of the FSO turbulent channel and decrease the power consumption. Moreover, the demonstrated DL models also work faster than maximum likelihood (ML) methods with perfect channel estimation decoders, with even slightly better performance because of the turbulence, thus enabling realization of FSO over turbulent atmospheric channels. INDEX TERMS Free space optical communication, deep learning, on-off keying modulation, amplitude shift keying modulation, maximum likelihood, channel state information, fully convolutional neural network, fully connected neural network, additive white gaussian noise, intensity modulation, direct detection, photodetector, bit error rate.

Free space optical communication (FSO) is widely deployed to transmit high data rates for rapid communication traffic increase. Asymmetrically clipped optical orthogonal frequency division multiplexing (ACO-OFDM) modulation is a very efficient FSO communication technique in terms of transmitted optical power. However, its performance is limited by atmospheric turbulence. When the channel includes strong turbulence or is non-deterministic, the bit error rate (BER) increases. To reach optimal performance, the ACO-OFDM decoder needs to know accurate channel state information (CSI). We propose novel detection using different deep learning (DL) algorithms. Our DL models are compared with minimum mean square error (MMSE) detection methods in different turbulent channels and improve performance especially for non-stationary and non-deterministic channels. Our models yield performance very close to that of the MMSE estimator when the channel is characterized by weak or strong turbulence and is stationary. However, when the channel is non-stationary and variable, our DL model succeeds in improving the performance of the system and decreasing the signal to noise ratio (SNR) by more than 8 dB compared to that of the MMSE estimator, and it succeeds in recovering the received data without needing to know accurate CSI. Our DL decoders also show notable speed and energy efficiency improvement.

The existing wired intra data center (DC) networks suffer from traffic congestion, low scalability, low flexibility, and increasing power consumption. To solve this problem there is a need to move from wired to wireless intra DC connections. One of the technologies that could provide a solution for the next generation intra DC communication is the use of free space optical (FSO) communication. However, in DC networks there is turbulence that can affect performance of FSO communication. In this letter, we present a DC experiment that we performed to characterize the performance of the FSO intra DC turbulent channel. We found that the pdf of the received signal is lognormally distributed according to the theoretical predictions, and we received values of bit error rate less than 10−16 in all the areas of the DC, which is better than the desired minimum of 10−14 for DC communication. This means that the realization of FSO communication within a turbulence channel DC is possible with good performance. If the turbulence is strong, we can improve the performance by using different mitigation techniques or by using hybrid wired and FSO connections.

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