# Improved Performance in the Detection of ACO-OFDM Modulated Signals Using Deep Learning Modules
## 1 Introduction
However, in real environments, such as communication inside data centers, underwater communication, and molecular communication, there is turbulence, which can affect detection of the recovered signals. In the regular estimation methods mentioned above, it is difficult to accurately extract CSI, because in some cases, the turbulence is strong, or the channel is variable and non-deterministic, which makes estimation difficult. Consequently, in these methods, it is necessary to transmit with more power or use other mitigation methods in order to achieve good performance. As a result, there is a need to build ACO-OFDM systems that can enable us to realize a complete communication system without having to know the parameters of the channel model in order to maximize the performance of the communication system. We demonstrate that the use of deep learning (DL) algorithms for recovering the transmitted data in ACO-OFDM modulated signals can be an effective solution for FSO through turbulent channels, thereby improving the performance of the communication system. Deep neural networks (DNNs) is one of the important algorithms that is widely used in DL. DNNs have tools that enable learning complicated models, which helps to optimize the performance of the entire system by learning relationships between input and output variables through training processes. In recent years, researchers have used DL models in different areas, such as image and speech recognition [14; 15], and wireless communication systems for encoding, decoding, channel estimation, and signal identification [16; 17; 18]. The use of DL in wireless communication systems can help improve system performance without the need to know the parameters of the channel or the encoder. In regular communication systems, the system is composed of several blocks, and one tries to maximize the performance of each block separately. However, the use of DL in wireless communication systems allows us to view the transmitter or the receiver as a single unit. The performance of the entire block is maximized by minimizing the bit error rate (BER), rather than trying to maximize each block separately as in regular encoders and decoders. This can improve the performance of the entire wireless communication system [18]. In recent years, researchers have used DL in different optical communication systems. In [19], researchers succeeded in using DL for reducing computational complexity in optical communication systems. In [20], the authors use it in detection through atmospheric turbulence for orbital angular momentum in FSO communication. The authors in [21] successfully used DL to mitigate fiber-induced nonlinearity. In [22], DL was used as a solution for the imperfect CSI problem in FSO communication systems. Recently, in [23], we used DL for improving the performance of on-off keying (OOK) modulation over different FSO turbulence channels. In this study, we show a new detection method of modulated ACO-OFDM signals using different models of DL. The noisy modulated transmitted data are recovered without the need to know any parameters about either the channel or the encoder. We examined the performance of the DL-assisted ACO-OFDM by generating random data bits modulated by ACO-OFDM. The data were transmitted via a different turbulent channel with additive white Gaussian noise (AWGN). The receiver receives the modulated data with noise and needs to recover the original data bits from the received data using different algorithms of DL, without the need to know anything about the modulation parameters or the parameters of the channel. The developed DL models were compared with one another and also with the regular ACO-OFDM decoder that uses the MMSE estimator, in terms of BER and computational and memory complexity that can affect the power consumption of the system. An improvement in the performance of ACO-OFDM, quantified by a decrease in the BER in all the DL models, was observed, especially over channels with strong or variable turbulence.