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

TL;DR

This paper demonstrates the integration of Reconfigurable Intelligent Surfaces (RIS) with the O-RAN framework in a real-time 5G environment, showing how RIS can enhance user performance and fairness through optimized control.

Contribution

It presents the design and implementation of RIS optimization xApps within O-RAN, and provides experimental validation in a multi-user 5G testbed.

Findings

RIS can boost individual user performance.

RIS can improve fairness among users.

RIS helps balance performance and fairness.

Abstract

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 utilized either to boost the performance of the selected user or to provide the fairness among the users or to balance the tradeoff between the performance and fairness.