# Improved Performance in the Detection of ACO-OFDM Modulated Signals Using Deep Learning Modules
## 1 Introduction  
Free space optical (FSO) communication has been widely used due to its capability to transmit with high data rates [1; 2], thus supporting the rapid increase in the amount of traffic passing through communication networks [3; 4]. The use of FSO communication has many advantages compared to optical fiber and radio frequency (RF) networks. FSO enables transmission with high data rates as in optical fibers, but in FSO communication, the data is transmitted by light in free space through the air and not by cable. This increases the flexibility of the networks compared to optical fiber networks, and it leads to dynamic wireless network capabilities, as well as decreasing the energy consumption of the communication networks, which is a very important consideration in our world. In addition to these, the installation of new FSO networks is easier and cheaper than the installation of new optical fiber networks. Furthermore, FSO communication is faster than RF; in FSO communication, there is no need for a spectrum license as in RF; and in FSO, there no need to use complicated security methods as in RF, because the data is transmitted along line of sight (LOS) paths and the wavelength is very small,which makes it difficult to eavesdrop. Therefore, FSO is more secure than RF and more resistant to RF interference [5; 6]. However, one of the most severe limitations on the performance of FSO in real environments is atmospheric turbulence, which stems from random refractions that lead to random fluctuations in the amplitude and phase of the received signal. Turbulence limits the use of FSO not only in the atmosphere, but also in data center environments where turbulence is derived from the local heating. In addition, recovering transmitted data at the receiver requires previous knowledge of the parameters of the encoder and decoder and accurate knowledge of the channel state information (CSI) of the turbulence in order to overcome noise [2; 5]. Recently, the use of optical orthogonal frequency division multiplexing (OOFDM) has been widely considered for FSO communication, because it is robust against inter symbol interference (ISI) and exhibits high spectral efficiency. In OOFDM, M-Quadrature Amplitude Modulation (M-QAM) is used for high bandwidth efficiency. However, OOFDM works with intensity modulation and direct detection (IM/DD), so only modulated signal intensity can be transmitted [6]. Therefore, the transmitted signal can only be bipolar and real, while in regular OFDM, the transmitted signals can be nonbipolar and complex. Many methods have been suggested for the implementation of OOFDM in FSO communication [7; 8; 9; 10; 11; 12]. One of these methods that can convert the transmitted signals to be bipolar and real without any loss of the transmission information is asymmetrically clipped optical orthogonal frequency division multiplexing (ACO-OFDM) modulation [8]. This is a very efficient technique for FSO in terms of transmitted optical power, more so than the methods that use data center (DC) biasing to convert the transmitted data to unipolar. Due to these advantages, we choose to investigate ACO-OFDM in this paper. In ACO-OFDM, the transmitted signals are made real by transmitting only the odd subcarriers, while the even subcarriers are set to zero [5; 6; 13]. However, in FSO communication, there is interference that derives from ambient light sources and turbulence. Therefore, estimating the CSI is important in order to successfully recover the data. We will suggest different techniques for estimating the CSI. The authors in [10] suggest using a least square (LS)-based channel estimator to achieve accurate results in ACO-OFDM FSO systems, while the authors in [11; 12] suggest using the minimum mean square error (MMSE) and linear MMSE (LMMSE). In the LS estimation method, optimal channel state estimation can be achieved when the channel is optimal, without noise and without interference. The accuracy with MMSE is better and higher than with LS but is limited due to the noise.