# Improved Performance in the Detection of ACO-OFDM Modulated Signals Using Deep Learning Modules
## 3 Our Proposed DL Detection Models  
DL is a neuron system model that maps input to output data through a graph that contains several layers. The input data are data with noise after passing through a specific system. The output of the DL system is the original data that the DL wants to detect. Each layer maps the data through nodes, where the first layer is the input data. Each layer depends on the value of the nodes in the previous layer after passing an activation function. DL tries to optimize the performance of the system through two stages. The first is called the training process, and the second is called the prediction process. At the beginning of the training process, the DL system model sets random values to the weights, which are the connections between the nodes in each of two consecutive neighboring layers, and then it changes the weights of the DL model by a gradient descent method in a number of iterations until it reaches the minimum loss between the output of the DL system and the original output. Then, after the training process, the system saves the final weights with the minimum loss and then starts an online process called the prediction process. In this process, the system receives online data and detects the original data, depending on the weights that were saved in the previous process. In recent years, many researchers have used DL for improving the performance of a system in many fields, including speech recognition, wireless communication, optical wireless communication, etc. In [23], we successfully used DL for improving the performance of OOK modulation over different FSO turbulent channels. The received data are a corrupted version of the data after passing through turbulence, and the output data are the original data bits that we want to detect. Here, we want to use the same concept as in [23], to improve the performance of modulated ACO-OFDM signals. We know that this modulation is similar to OOK but is more sophisticated. In both modulation systems, the DL system receives modulated data after it has passed through a turbulent channel with noise and is required to recover the original data bits. In this section, we propose a new DL detection method for ACO-OFDM for FSO communication to enable its use over different turbulent channels and to improve the performance of the FSO communication system when the channel is characterized by strong turbulence or is variable, without the need to know anything about the parameters of the channel. The system of our model is presented in Figure 2.  
In order to use DL systems, we need to learn through a training process, which requires a dataset. For this, we build a dataset by generating random data bits and modulating them by ACO-OFDM modulation, and then, we send them through different atmospheric turbulent channels with AWGN. The receiver recovers the modulated data via a regular ACO-OFDM decoder using an MMSE estimator. For each generated dataset, we create and save two vectors: the input and output vectors. The input vector is the input to the DL system that contains the input data to the ACO-OFDM receiver, which is the modulated data with AWGN after passing through the turbulent channel. The output vector is the original generated data bits. In the training process, the model learns and changes the weights  
Figure 2: The proposed system. of the DL model until it reaches the minimum loss condition between the output of the DL system and the original output. Then, after the training process, the system saves the final weights with the minimum BER and starts an online process. In the online process, the system can receive any ACO-OFDM modulated data with AWGN after passing through the turbulent channel and detect the original data bits, depending on the weights. In our work, we used different DL models. In the first model, we used a fully connected (FC) neural network, and in the second model, we used a fully convolutional neural network (FCNN). Schemes of the DL models that we built are presented in Figures 3 and 4.