Technology Category
Intelligent Robots: Multi-Robot SystemsMachine Learning: Hardware-aware MLPlanning, Routing, and Scheduling: Plan Execution and Monitoring
System-Level Experimental Evaluation of Reconfigurable Intelligent Surfaces for NextG Communication Systems
🤖 AI Summary
Existing RIS research is largely confined to theoretical modeling and physical-layer analysis, lacking experimental validation that integrates realistic channel characteristics and full protocol-stack implementation—thus hindering cross-band (Sub-6 GHz/mmWave) and multi-scenario (eMBB/URLLC) performance evaluation.
Method: This work presents the first full protocol-stack hardware-in-the-loop experiment for RIS-empowered systems: (i) a geometry-based stochastic channel model built upon QuaDRiGa; (ii) a single-leader–multiple-followers Stackelberg game framework jointly optimizing RIS phase shifts and power allocation.
Contribution/Results: The framework is validated on Colosseum—the world’s largest wireless emulation platform—enabling the first end-to-end RIS stack evaluation. Results demonstrate substantial energy efficiency gains at mmWave frequencies; at Sub-6 GHz, a compact RIS of only ~100 elements significantly reduces URLLC latency, highlighting its superiority in resource-constrained deployments.
Technology Category
Application Category
📝 Abstract
Reconfigurable Intelligent Surfaces (RISs) are a promising technique for enhancing the performance of Next Generation (NextG) wireless communication systems in terms of both spectral and energy efficiency, as well as resource utilization. However, current RIS research has primarily focused on theoretical modeling and Physical (PHY) layer considerations only. Full protocol stack emulation and accurate modeling of the propagation characteristics of the wireless channel are necessary for studying the benefits introduced by RIS technology across various spectrum bands and use-cases. In this paper, we propose, for the first time: (i) accurate PHY layer RIS-enabled channel modeling through Geometry-Based Stochastic Models (GBSMs), leveraging the QUAsi Deterministic RadIo channel GenerAtor (QuaDRiGa) open-source statistical ray-tracer; (ii) optimized resource allocation with RISs by comprehensively studying energy efficiency and power control on different portions of the spectrum through a single-leader multiple-followers Stackelberg game theoretical approach; (iii) full-stack emulation and performance evaluation of RIS-assisted channels with SCOPE/srsRAN for Enhanced Mobile Broadband (eMBB) and Ultra Reliable and Low Latency Communications (URLLC) applications in the worlds largest emulator of wireless systems with hardware-in-the-loop, namely Colosseum. Our findings indicate (i) the significant power savings in terms of energy efficiency achieved with RIS-assisted topologies, especially in the millimeter wave (mmWave) band; and (ii) the benefits introduced for Sub-6 GHz band User Equipments (UEs), where the deployment of a relatively small RIS (e.g., in the order of 100 RIS elements) can result in decreased levels of latency for URLLC services in resource-constrained environments.
Problem
Research questions and friction points this paper is trying to address.
Evaluating RIS benefits for NextG systems via full-stack emulation.
Optimizing energy efficiency and power control with RIS.
Modeling RIS-enabled channels accurately across spectrum bands.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Geometry-Based Stochastic Models for RIS channel modeling
Stackelberg game theory for optimized resource allocation
Full-stack emulation with SCOPE/srsRAN for performance evaluation
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