RIS Control through the Lens of Stochastic Network Calculus: An O-RAN Framework for Delay-Sensitive 6G Applications

2026-02-19

AI Summary

This work addresses the challenge of supporting ultra-reliable low-latency communication (URLLC) in 6G networks, where existing multi-RIS control schemes struggle to adapt to dynamic network conditions and meet heterogeneous user QoS requirements. To this end, the authors propose the DARIO framework, which dynamically allocates RIS resources over short time windows within the O-RAN architecture. DARIO introduces stochastic network calculus (SNC) into RIS scheduling for the first time, enabling an analytically tractable model for delay-bound estimation. An online heuristic algorithm is developed to efficiently solve the resulting nonlinear integer programming problem, significantly reducing control overhead while enabling rapid response to traffic fluctuations. Simulation results demonstrate that, under high-load or RIS resource-constrained scenarios, DARIO reduces end-to-end latency by up to 95.7%, effectively guaranteeing diverse per-user latency and reliability targets.

Technology Category

• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Reasoning under Uncertainty: Stochastic Optimization
• Search and Optimization: Distributed Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Algorithmic accountability and transparency on the web
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

Reconfigurable Intelligent Surfaces (RIS) enable dynamic electromagnetic control for 6G networks, but existing control schemes lack responsiveness to fast-varying network conditions, limiting their applicability for ultra-reliable low latency communications. This work addresses uplink delay minimization in multi-RIS scenarios with heterogeneous per-user latency and reliability demands. We propose Delay-Aware RIS Orchestrator (DARIO), an O-RAN-compliant framework that dynamically assigns RIS devices to users within short time windows, adapting to traffic fluctuations to meet per-user delay and reliability targets. DARIO relies on a novel Stochastic Network Calculus (SNC) model to analytically estimate the delay bound for each possible user-RIS assignment under specific traffic and service dynamics. These estimations are used by DARIO to formulate a Nonlinear Integer Program (NIP), for which an online heuristic provides near-optimal performance with low computational overhead. Extensive evaluations with simulations and real traffic traces show consistent delay reductions up to 95.7% under high load or RIS availability.

Problem

• Reconfigurable Intelligent Surfaces
• ultra-reliable low latency communications
• delay minimization
• 6G networks
• heterogeneous latency demands

Innovation

• Reconfigurable Intelligent Surfaces
• Stochastic Network Calculus
• O-RAN
• Delay-Aware Scheduling
• Nonlinear Integer Programming