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O-RIS-ing: Evaluating RIS-Assisted NextG Open RAN

Maria Tsampazi, Michele Polese, Falko Dressler, Tommaso Melodia

TL;DR

The paper tackles the challenge of boosting Open RAN performance by integrating Reconfigurable Intelligent Surfaces (RIS) to shape wireless propagation. It develops an RIS-based optimization framework and evaluates it on the Colosseum testbed using OpenRAN Gym, deploying an xApp to control the BS scheduling policy and exploring three resource-allocation scenarios across eMBB and URLLC slices. Key findings show that RIS-assisted topologies consistently improve throughput and reduce latency, with up to approximately 34% throughput gains in multi-user settings and substantial decreases in buffer occupancy, signaling improved resource utilization. The work demonstrates the practical viability of RIS-enabled, Open RAN deployments and highlights avenues for dynamic, real-time control of scheduling policies and per-slice weights to further enhance performance.

Abstract

Reconfigurable Intelligent Surfaces (RISs) pose as a transformative technology to revolutionize the cellular architecture of Next Generation (NextG) Radio Access Networks (RANs). Previous studies have demonstrated the capabilities of RISs in optimizing wireless propagation, achieving high spectral efficiency, and improving resource utilization. At the same time, the transition to softwarized, disaggregated, and virtualized architectures, such as those being standardized by the O-RAN ALLIANCE, enables the vision of a reconfigurable Open RAN. In this work, we aim to integrate these technologies by studying how different resource allocation policies enhance the performance of RIS-assisted Open RANs. We perform a comparative analysis among various network configurations and show how proper network optimization can enhance the performance across the Enhanced Mobile Broadband (eMBB) and Ultra Reliable and Low Latency Communications (URLLC) network slices, achieving up to ~34% throughput improvement. Furthermore, leveraging the capabilities of OpenRAN Gym, we deploy an xApp on Colosseum, the world's largest wireless system emulator with hardware-in-the-loop, to control the Base Station (BS)'s scheduling policy. Experimental results demonstrate that RIS-assisted topologies achieve high resource efficiency and low latency, regardless of the BS's scheduling policy.

O-RIS-ing: Evaluating RIS-Assisted NextG Open RAN

TL;DR

The paper tackles the challenge of boosting Open RAN performance by integrating Reconfigurable Intelligent Surfaces (RIS) to shape wireless propagation. It develops an RIS-based optimization framework and evaluates it on the Colosseum testbed using OpenRAN Gym, deploying an xApp to control the BS scheduling policy and exploring three resource-allocation scenarios across eMBB and URLLC slices. Key findings show that RIS-assisted topologies consistently improve throughput and reduce latency, with up to approximately 34% throughput gains in multi-user settings and substantial decreases in buffer occupancy, signaling improved resource utilization. The work demonstrates the practical viability of RIS-enabled, Open RAN deployments and highlights avenues for dynamic, real-time control of scheduling policies and per-slice weights to further enhance performance.

Abstract

Reconfigurable Intelligent Surfaces (RISs) pose as a transformative technology to revolutionize the cellular architecture of Next Generation (NextG) Radio Access Networks (RANs). Previous studies have demonstrated the capabilities of RISs in optimizing wireless propagation, achieving high spectral efficiency, and improving resource utilization. At the same time, the transition to softwarized, disaggregated, and virtualized architectures, such as those being standardized by the O-RAN ALLIANCE, enables the vision of a reconfigurable Open RAN. In this work, we aim to integrate these technologies by studying how different resource allocation policies enhance the performance of RIS-assisted Open RANs. We perform a comparative analysis among various network configurations and show how proper network optimization can enhance the performance across the Enhanced Mobile Broadband (eMBB) and Ultra Reliable and Low Latency Communications (URLLC) network slices, achieving up to ~34% throughput improvement. Furthermore, leveraging the capabilities of OpenRAN Gym, we deploy an xApp on Colosseum, the world's largest wireless system emulator with hardware-in-the-loop, to control the Base Station (BS)'s scheduling policy. Experimental results demonstrate that RIS-assisted topologies achieve high resource efficiency and low latency, regardless of the BS's scheduling policy.

Paper Structure

This paper contains 13 sections, 7 equations, 6 figures, 1 table.

Table of Contents

  1. Introduction
  2. Contributions and Outline
  3. System Model
  4. Open RAN Framework
  5. Channel Model
  6. Optimizing Wireless Propagation with RIS
  7. Resource Allocation Strategies
  8. Experimental Setup
  9. Experimental Evaluation
  10. Single-UE Case Study A
  11. Multi-UE Case Study B
  12. Multi-UE O-RAN Case Study C
  13. Conclusions and Future Work

Figures (6)

  • Figure 1: Reference O-RAN testing architecture for -enabled deployments, focusing on the Sched xApp Case Study, as described in Section \ref{['multiue-secc']}.
  • Figure 2: Path Gains under varying numbers of elements.
  • Figure 3: Performance evaluation results for the single- Case Study.
  • Figure 4: Performance evaluation results for the multi- Case Study.
  • Figure 5: Performance evaluation results for the multi- O-RAN Case Study.
  • ...and 1 more figures