Performance Evaluation of Statistical Reconfigurable Intelligent Surface Adjustment Algorithms

Reconfigurable Intelligent Surface (RIS) is a promising technology that can improve the throughput and extend the coverage area of wireless networks. The efficiency of RIS depends on its configuration, which is adjusted taking into account the spatial positions of transmitting and receiving devices, as well as channel information. Among the various classes of RIS adjustment algorithms, statistical adjustment can be classified into separate class. Algorithms from this class allow RIS to be adjusted using only signal power. This allows using these algorithms with RIS in already deployed cellular communication systems. Statistical adjustment algorithms do not imply any input data or system models except for received signal power. Therefore, they can be easily implemented in plug-and-play mode. In this paper, we investigate the performance of various statistical RIS adjustment algorithms. Namely, we consider the RIS adjustment rate, the dependence of the signal-to-noise ratio on the number of control bits of the RIS element, and on the noise level.