## 1 Introduction  
The growth of the fifth-generation (5G) mobile communication has caused a significant bandwidth demand. In wireless networks, greater frequency bands are allotted and used to enhance higher data rates and system capacity. The 5G New Radio (5G NR) is given to mmWave frequencies between 24 GHz and 50 GHz. This technical development is expected to lead to the employment of the sub-THz bands of 114-300 GHz in wireless communication systems beyond the (B5G) or 6G systems [1]. For the next B5G or 6G wireless networks, the reconfigurable intelligent surface (RIS) is being studied as a viable new technology [2]. The real-time programmable controller in RIS is controlled by a number of passive devices that may independently alter the phase of electromagnetic wave propagation [3, 4]. The RIS can be implemented in the walls of the inside and outside environment of the buildings and windows glasses [5; 6; 7]. The usage of RIS might be used to enhance wireless communications by removing channel blockages, boosting non-line-of-sight connections, expanding the coverage, and lowering inter-user interference (IUI). As each of its components has the capacity to alter the phase and amplitude of the incoming signal, RIS may offer passive beam-forming (BF) [8] by modifying the phase shift and amplitude reflection coefficients of its constituent parts. In contrast to the conventional multiple-input multiple-output (MIMO) systems, which place a greater emphasis on the base station (BS) and user BF, the RIS-assisted communication system requires the joint design of active BF for BS and user, as well as passive BF for RIS, in order to achieve the passive BF gain. Additionally, RIS-assisted systems enhance spectral efficiency (SE) and energy efficiency (EE) while using less money and little power. This is implemented by employing large-scale passive reflecting components [9; 10; 11]. The literature has recently been devoted to several research activities covering various aspects, such as channel modeling [12], channel (CE) estimation [13], modulation and encoding [14], SE analysis [6; 15], outage probability [16], symbol error probability (SER) [17], energy efficiency [18], weighted sum rate [19; 20], and the evaluation of performance in RIS-supported wireless networks [21]. This is because RIS-enabled settings provide significant potential advantages. Deep learning (DL) approaches have the potential to significantly improve wireless communication performance [22; 23; 24; 25; 26]. Many works were performed in RIS-based wireless systems to handle problems related to channel state information (CSI) [27], CE [28], and performance maximization [29; 30] using the DL approaches. To avoid performance loss using a DL model, in [29], the authors proposed an end-to-end training-based RIS-assisted MIMO system. The proposed system optimized the signal processing operations at the access point, RIS, and user at the same time, with active BF for the access point and user and passive BF for RIS. For estimating and identifying symbols in signals sent by RIS, the authors of [30] designed a DL method that employs fully connected layers for prediction when estimating channels and phase angles from a reflected signal received through a RIS. In [31], the authors proposed an effective RIS-assisted MIMO system based on a DNN multi-stage training strategy model for perfect CSI acquisition. The authors in [32] proposed a deep denoising neural-network-assisted compressive sensing broadband for the mmWave RIS system. An indoor RIS-based communication environment was considered in [33], where a trained DNN was used to determine the optimal phase shift by using the target user position. A deep reinforcement learning framework was proposed in [34] for predicting the RIS reflection matrix with minimal beam training overhead. For natural language processing and machine vision, the convolutional neural network (CNN) model is basically used. It can extract the high level of features from the input and also can be used for the time-series data processing [35].