## 2 Related Works
However, this is not readily applicable to the higher frequency bands. Furthermore, [PERSON]'s equations and the reciprocity theorem have been used to determine the transfer function of the observed RFI in radio links [22]. Additionally, a decision tree model incorporating a machine learning algorithm has been deployed to suppress RFI in nuclear quadrupole resonance (NOR) data [23]. However, this methodology may not apply to low-level or continuous background RFI scenarios. Reconfigurable intelligent surfaces (RIS) are currently gaining widespread popularity as enabling technologies to tackle the vast RFI issues in wireless networks. RIS is envisioned to enable 6G wireless networks similarly to the use of massive MIMO in 5G wireless networks [12, 13]. In the recent literature [24, 25], RISs have been identified as candidate technologies to improve the performance of wireless communication networks with minimal or no interference. Interestingly, RIS is relatively passive, requiring fewer active antennas at the BS to achieve massive MIMO gains [26]. This lends credence to the high energy efficiency of RIS [27]. Generally, RISs, owing to the passive elements used in their design, can mitigate the impact of interference in an ultra-dense wireless network [28]. Comparatively, an RIS has a meager cost relative to other relay-based technologies. RIS is sometimes referred to as large intelligent surfaces [29, 30]. Moreover, RIS facilitates low-power device-to-device (D2D) communication, leading to the actualization of simultaneous wireless information and power transfer (SWIPT) [28, 31]. They are often referred to as reconfigurable intelligent surfaces [27, 32] and software-controlled metasurfaces [33]. Currently metasurfaces are being designed with the conventional reflect arrays [34, 35], liquid crystal arrays [36], or software-defined metamaterials [33, 37]. Due to their passive nature, RISs can easily be deployed on buildings and other structures. RISs can be configured to achieve security at the physical layer [28, 38]. A typical RIS-assisted wireless communication system that reflects signals from the RIS to facilitate seamless communication between the BS, and the mobile station is given in Figure 1[39]. An analysis of RIS features compared to existing relay systems is presented in Table 1[39]. Figure 1: Illustration of a Reconfigurable Intelligent Surface (RIS)-assisted communication system. Despite the auspicious potentials of RIS in facilitating beyond 5G wireless communication systems, several challenges have been identified [41, 42]. Specifically, the phase range of unit cells making up the aperture is relatively limited [41]. This implies that a complex cell topology would be required to achieve a full-phase range. Consequently, this tends to increase the hardware complexity [42]. Additionally, real-time control of the reflection amplitude and phase shift of RIS could be computationally expensive [14]. Currently, RIS lacks radio resources for perfect channel estimation [43]. This makes it quite challenging to estimate channel state information [25, 44]. A low-power RF chain-empowered RIS capable of channel estimation has been proposed [28] to alleviate this limitation. Interestingly, a ray-tracing technique [45] was recently presented to estimate the channel information by [32], and machine learning, deep learning, and federated learning models have been provided to tackle these challenges at reduced costs [46, 47, 48]. However, the lack of adequate test and training data has limited the use of machine learning-based RIS schemes. The related literature, including the key contributions, findings, and limitations, are summarized in Table 2.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Refs.