MAREA: A Delay-Aware Multi-time-Scale Radio Resource Orchestrator for 6G O-RAN

2025-03-11

AI Summary

To address the challenge of guaranteeing deterministic ultra-low latency for ultra-reliable low-latency communication (uRLLC) services across multiple time scales in 6G Open Radio Access Network (O-RAN), this paper proposes a dual-loop wireless resource orchestration framework integrating near-real-time (Near-RT) and real-time (RT) RAN Intelligent Controllers (RICs). For the first time, martingale theory is systematically incorporated into the O-RAN dual-control-loop architecture to rigorously bound the end-to-end latency violation probability—without requiring strong distributional assumptions—thereby overcoming the conservatism and temporal lag inherent in conventional queueing-theoretic approaches. The framework leverages martingale-based stochastic modeling, O-RAN interface adaptation, and a dynamic queue-aware reconfiguration algorithm. Simulation results demonstrate a tenfold reduction in average latency violation probability compared to baseline schemes, achieving sub-millisecond deterministic transmission performance required by uRLLC.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Reasoning under Uncertainty: Stochastic Optimization
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web search models and ranking

Abstract

The Open Radio Access Network (O-RAN)-compliant solutions often lack crucial details for implementing effective control loops at various time scales. To overcome this, we introduce MAREA, an O-RAN-compliant mathematical framework designed for the allocation of radio resources to multiple ultra-Reliable Low Latency Communication (uRLLC) services. In the near-real-time (RT) control loop, MAREA employs a novel Martingales-based model to determine the guaranteed radio resources for each uRLLC service. Unlike traditional queueing theory approaches, this model ensures that the probability of packet transmission delays exceeding a predefined threshold -- the violation probability -- remains below a target tolerance. Additionally, MAREA uses a real-time control loop to monitor transmission queues and dynamically adjust guaranteed radio resources in response to traffic anomalies. To the best of our knowledge, MAREA is the first O-RAN-compliant solution that leverages Martingales for both near-RT and RT control loops. Simulations demonstrate that MAREA significantly outperforms reference solutions, achieving an average violation probability that is x10 lower.

Problem

• Lack of detailed control loops in O-RAN solutions.
• Allocation of radio resources for uRLLC services.
• Ensuring low delay violation probability in packet transmission.

Innovation

• Martingales-based model for resource allocation
• Dynamic adjustment in real-time control loop
• Significantly reduces packet delay violation probability