RIS Meets O-RAN: A Practical Demonstration of Multi-user RIS Optimization Through RIC

Open Radio Access Network (O-RAN) along with artificial intelligence, machine learning, cloud, and edge networking, and virtualization are important enablers for designing flexible and software-driven programmable wireless networks. In addition, Reconfigurable Intelligent Surfaces (RIS) represent an innovative technology to direct incoming radio signals toward desired locations by software-controlled passive reflecting antenna elements. Despite their distinctive potential, there has been limited exploration of integrating RIS with the O-RAN framework, an area that holds promise for enhancing next-generation wireless systems. This paper addresses this gap by designing and developing the RIS optimization xApps within an O-RAN-based real-time 5G environment. We perform extensive measurement experiments using an end-to-end 5G testbed including the RIS prototype in a multi-user scenario. The results demonstrate that the RIS can be effectively utilized to boost the received signal power of the selected user or provide fairness among the users. This is a promising result demonstrating that RIS can support high-level policies in a multi-user network scenario.