## 1 Introduction
### Related Works  
In [6], the authors introduced DL-based detector for the multiuser downlink OFDM-NOMA system. The authors mainly depended on pilot signals for the channel information, and according to these pilot responses, a DL based joint channel estimation and symbol detection was achieved without additional processing for channel estimation. The simulation's outcomes revealed that the proposed DL scheme outperforms the conventional SIC-based detector. On the other hand, the proposed scheme needed to be initially trained offline for different channel conditions and the simulation results were presented in terms of BER only. In [7], the authors suggested a deep learning framework to perform signal recovery in the MIMO-NOMA system when the Rayleigh fading channel is considered. The proposed technique can simultaneously carry out the channel estimation process and signal detection. Simulations were conducted for the proposed DL scheme, and the results were compared with the conventional SIC procedure in terms of the symbol error rate (SER) and throughput. According to the simulation results, the proposed DL scheme can address channel impairment, but the examined NOMA cell was limited for two users and an offline training stage was also required. Also, the DNN training phase needed two components, the received signal, and the labels, which were used as supervised data to help the DNN to optimize the parameters. In [8], the authors proposed a data-driven deep learning estimator for time- and frequency-selective channels. The proposed algorithm was designed such that a pre-training scheme and pilot symbols were utilized as inputs for the DNN to attain a desired initialization, which can further enhance the performance of the DL estimator. The DNN was trained offline in both the pre-training and training stages, while in the testing stage, the channels could be dynamically tracked by the DNN with only pilots identified, and then the transmitted symbols were detected. The performance of a DL estimator with different numbers of layers was investigated and the numerical results demonstrated that the proposed DL estimator outperformed the standard channel estimator in terms of efficiency and robustness. In [9], a deep learning approach was employed to estimate the downlink channel and to reduce the training overhead in a fog radio access network. The Gated Recurrent Unit (GRU) was utilized to learn the hidden correlations among the channel matrices from different users, and a bidirectional GRU was also employed to further improve the estimation performance. Simulation results were provided to demonstrate the performance gains, but the examined performance metrics were limited to the loss function and mean square error. Based on the deep learning (DL) algorithm, the authors in [10] introduced a sliding window Gated Recurrent Unit (GRU) channel estimator to acquire knowledge for the time-varying Rayleigh fading channel. Interleaver and channel coding schemes were merged with the proposed sliding window estimator to further enhance system performance. The simulation results proved the ability of the suggested procedure to follow the channel in a reliable way and achieve better mean square error (MSE). Moreover, the sliding window-based GRU estimator was examined with different numbers of pilot symbols, and the robustness against the variations in the channel characteristics was analyzed. In [11], the authors went to conclude that DL algorithm can be utilized in signal detection for uplink analysis in NOMA network. The authors proposed a DL approach to characterize the complex channel parameters, where restricted Boltzmann machines (RBM) were implemented as a pre-training phase for the original input sequence for the network. The proposed learning scenario based on LSTM layer could track the environment statistics automatically via offline learning and an iterative support detection procedure was suggested to identify the transmitted symbols.