Machine learning approaches of large intelligent surface to assist 6G-IoT applications: a holistic review

Wireless networks of 5G and beyond require accommodating a wide range of services and applications, a rapidly increasing data traffic flow across handheld devices, and a growing density of mobile users. In future networks, high-frequency waves will be crucial, but they are weak over long distances and can be easily blocked by objects. Through a tutorial, a comparison of the implementation and capabilities of IRS, large intelligent surfaces (LIS), and RIS are carried out. Future wireless networks may be able to increase their coverage and data rate by utilizing LISs. This is how the idea of a smart radio environment is realized. As such, LISs have the infrastructure to support the sixth generation of communication networks. The potential benefits of RIS-assisted communication systems can be effectively maximized through machine learning (ML), especially when the computational complexity of LIS deployment and operation rises quickly as the number of user-infrastructure interactions begins to increase.