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

TL;DR

This paper introduces DARIO, an O-RAN-compliant framework utilizing stochastic network calculus to dynamically assign RIS devices in 6G networks, significantly reducing delays for ultra-reliable low latency communications.

Contribution

It presents a novel delay-aware RIS control framework that adapts to traffic conditions using a stochastic network calculus model and an efficient heuristic solution.

Findings

Achieves delay reductions up to 95.7% in simulations.

Effectively adapts to traffic fluctuations and RIS availability.

Provides analytical delay bounds for RIS-user assignments.

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.