# A Survey on the Impact of Intelligent Surfaces in the Terahertz Communication Channel Models
## 1 Introduction
### Related Works
Each reflector can induce an amplitude and/or phase change to the incident signal independently. Thus, enhancing wireless communication performance by modifying the wireless channel in a so-called intelligent and programmable wireless environment. The use of IS can benefit the THz network in different aspects and improve the quality of communication and services. IS is a technology that enables the control of channel multipath, shadowing, and path loss. These phenomena cause the fading to which the signal is subjected in a wireless communication system. They randomly introduce constructive and destructive interference, delay, and phase shift in the propagated signal. To control the effects of the environment on the communication channel, an IS performs reflection and precise steering of the signal. In order to evaluate the RIS performance, the authors in [29] studied deterministic path loss and its dependency on the operating frequency, transmission distance, relative humidity, air temperature, and pressure. Curiously, the work in [30] proposed an IRS to control the propagation direction of the THz beam. The idea has been implemented through an efficient Taylor expansion-assisted gradient descent (TE-GD) scheme by dynamically updating the step size in the iterative process. Improved coverage performance has been achieved by intelligently adjusting the phase changes of the reflective elements. Unlike [30], in [31], an analytical model of path loss for RIS-assisted THz wireless systems was presented. The formalized proposal showed in detail the parameters that affect the molecular absorption and, consequently, the communication in THz. These parameters can be the transmission frequencies from the access point (AP) to the RIS center and from the RIS center to the users. Since the multiple-input multiple-output (MIMO) technique was essential for meeting the 6G requirements, in [32], the authors performed an IRS-assisted MIMO study considering a spherical wave channel modeling. The model regarding power gain and energy efficiency (EE) has been analyzed. It has been shown that the IRS can significantly improve the MIMO EE when operating in the near-field. An investigation of the channel estimation and transmission solutions for a mMIMO IRS-assisted THz system was conducted in [33]. The channel estimation was realized by beam training, and the quantization error was analyzed to evaluate performance. To overcome some communication problems in the THz band, it is worth mentioning that channel state information (CSI) can improve the transmitter decision-making and optimize the transmission rate. Some authors have considered a perfect knowledge of the CSI, which is not observed in practice. In contrast, in [34], the authors proposed a channel estimation scheme based on compressed sensing (CS). Essentially, it purposely performed subspace search iterative atom pruning-based subspace pursuit (IAP-SP) for the acquisition of CSI. Channel recovery performance and the level of complexity compared to conventional models were analyzed. This proposal reduced up to 99.51% of the complexity level compared to the conventional ones. Different from [34], in [35], in addition to exploring channel estimation, it has maximized the data rate in a IRS-assisted THz MIMO system. Similarly, the proposed IAP-SP algorithm reduced computational complexity while maintaining accurate channel recovery. The optimal data rate of the proposed system was found at the cost of an extremely high computational load. A new deep neural network (DNN) scheme based on the fully connected feed-forward structure has achieved near-optimalcommunication rate performance and greatly reduced the computational complexity of the system. Beyond the good results obtained by the authors mentioned previously, the link outage probability (OP) is one of the metrics to assess performance. Interestingly, the authors in [59] proposed a new swarm intelligence-based method to optimize phase changes in RIS elements under discrete constraints.