## 1 Introduction
### Related Works  
Different techniques were introduced by authors in [10] to realize the optimal MMSE channel estimator in the Reconfigurable Intelligent Surfaces (RIS)-based MISO system. In the first technique, the authors suggest an analytical linear estimator to adjust the phase shift matrix of the RIS during the training phase, and the estimator based on that technique is shown to produce sensible accuracy compared to the least-squares method when the statistical properties of the applied channel and noise are considered. In the other approach, authors have expressed the channel prediction problem as an image denoising problem, then they introduce a Convolutional Neural Network (CNN) to achieve the denoising and predict the optimal MMSE channel parameters. Numerical outcomes have clarified that the proposed estimator based CNN algorithm can offer improved performance compared to the linear estimation method and low computational intricacy is preserved. Toward enhancing the link reliability, a neural network model for a wireless channel estimator is proposed in [11] to be used with uncoded space-time diversity procedure in Multi Input Multi Output (MIMO) system. Based on the neural network ML structure, a channel estimator is suggested, and a mathematical scheme is presented to derive an optimum power transmission factors that can assist in lessening the channel prediction bandwidth utilization. Simulation results revealed that the channel estimator based on the proposed neural network structure can deliver an improvement in Bit Error Rate (BER) and Mean Square Error (MSE) compared to the standard MMSE channel estimation technique. In a massive MIMO system and on the basis of a deep autoencoder scheme, authors in [12] performed experimental verifications on two tasks, one task for channel estimation modelling for wireless links, and the other task is belonging to a power allocation policy. The proposed deep learning autoencoder is also used to manage the issue raised from inadequate training datasets that may cause critical overfitting problems and consequently affect the model's reliability. Results based on the autoencoder procedure clarified that the suggested scheme could successfully enhance performance when the extent of the training dataset is mainly within a specified threshold selection. To get over limitations raised when standard iterative power control techniques are utilized, such as high complexity and unnecessary latency, the work in [13] introduced a deep learning framework to manage these issues. In the presented structure, the outdated and partial CSI is exploited, and a Deep Neural Network (DNN) framework is created to construct an optimization problem to boost the spectral efficiency in device-to-device communication systems. User fairness and energy efficiency constraints were examined, and simulation outcomes showed that the proposed DNN model can attain better spectral and energy efficiency compared to the MMSE procedure when numerous channel correlation factors are considered. Based on CSI, the position of each user device with respect to BS, and the path loss, a deep learning framework labelled PowerNet is introduced in [14]. The authors attempt to prove that it is possible to avoid the time consumption involved with intricate channel estimation procedures, and at the same time, power control can be managed. Differentfrom traditional DNNs that employ a fully connected structure, the presented PowerNet method utilizes a CNN layers to recognize the interference model through several links in wireless networks. Simulation outcomes revealed that the suggested PowerNet scheme can realize a stable performance without explicit channel estimation. Recently, approximating the channel parameters or predicting the power factors with the assistance of Reinforcement learning (RL), is investigated by many researchers. The authors of [15] proposed an end-to-end channel estimation framework for a downlink multiuser multiple antenna system.