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

Oscar Adamuz-Hinojosa, Lanfranco Zanzi, Vincenzo Sciancalepore, Marco Di Renzo, Xavier Costa-Pérez

TL;DR

This work tackles uplink delay guarantees in multi-RIS 6G scenarios by introducing DARIO, an O-RAN–compliant delay-aware RIS orchestrator. It combines a novel Stochastic Network Calculus (SNC) delay-bound model with a nonlinear integer program and an online two-stage heuristic to dynamically assign RISs to UEs across scheduling periods, achieving per-user delay targets under stochastic traffic and channel dynamics. The key contributions include detailed DARIO architecture (RO interfaces and SNC module), a configuration-dependent SNC service model, a practical UE-RIS-RB assignment formulation, and a comprehensive evaluation showing up to 95.7% delay reduction under heavy load or high RIS availability, validated with simulations and real urban traces. The results demonstrate the practicality of real-time, delay-aware RIS control within the O-RAN ecosystem for delay-sensitive 6G applications.

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.

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

TL;DR

This work tackles uplink delay guarantees in multi-RIS 6G scenarios by introducing DARIO, an O-RAN–compliant delay-aware RIS orchestrator. It combines a novel Stochastic Network Calculus (SNC) delay-bound model with a nonlinear integer program and an online two-stage heuristic to dynamically assign RISs to UEs across scheduling periods, achieving per-user delay targets under stochastic traffic and channel dynamics. The key contributions include detailed DARIO architecture (RO interfaces and SNC module), a configuration-dependent SNC service model, a practical UE-RIS-RB assignment formulation, and a comprehensive evaluation showing up to 95.7% delay reduction under heavy load or high RIS availability, validated with simulations and real urban traces. The results demonstrate the practicality of real-time, delay-aware RIS control within the O-RAN ecosystem for delay-sensitive 6G applications.

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.
Paper Structure (29 sections, 29 equations, 9 figures, 3 tables, 2 algorithms)

This paper contains 29 sections, 29 equations, 9 figures, 3 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. DARIO Framework
  3. Architecture, Functionalities, and Interfaces
  4. Orchestration Workflow within O-RAN Control Loops
  5. Implementation Challenges for Integration
  6. Quantitative Considerations on Control-Loop Latency and Signaling Overhead
  7. Scalability of DARIO in Multi-Cell Environments
  8. System Model
  9. Radio Resource Scheduling Model
  10. Channel Model Scenario 1: Non- Assignment
  11. Channel Model Scenario 2: RIS Assignment
  12. Per-UE Delay Bound Modeling via Stochastic Network Calculus
  13. Introduction to Stochastic Network Calculus (SNC)
  14. Methodology for Delay Bound Derivation with SNC
  15. UE Traffic Model for the Arrival Process
  16. ...and 14 more sections

Figures (9)

  • Figure 1: DARIO integrated within the architecture.
  • Figure 2: Estimated vs. real delay bounds across multiple validation scenarios (VS). VS1: varying number of RBs, UE distances, and RIS assignment; VS2: fixed RBs, varying violation probability; VS3: fixed RBs and violation probability, varying UE positions with/without RIS; VS4: fixed UE positions, varying RIS assignment probability.
  • Figure 3: Delay bound $W_u$ versus UE--BS distance for different phase quantization levels $B$ and numbers of reflecting elements $L$ ($\varepsilon_u=10^{-3}$, $N_u^{RB}=$ 5 per slot).
  • Figure 4: Top plot: Convergence of the objective function with the number of scheduling periods$|\mathcal{T}_{ i}|$. Bottom plot: CDF of the objective function values over $|\mathcal{I}|=1800$ assignment periods.
  • Figure 5: Boxplot representation of the distribution of the execution time of DARIO for different network scenarios.
  • ...and 4 more figures