DetO-RAN: Dual-Timescale Resource Allocation for Flows in Deterministic Open RAN
Deterministic transmission and computation are essential for open radio access networks (O-RANs) to support latency-critical and computation-intensive applications. However, existing O-RAN and time-sensitive networking integrations mainly focus on deterministic guarantees in wired fronthaul transmission, while lacking a unified mechanism to coordinate wireless transmission, wired transmission, and computation. This limitation makes it difficult to provide bounded end-to-end latency under time-varying wireless channels and increasing computational demands, which reduces flow scheduling success rates and causes resource wastage. In this paper, we propose a hierarchical deterministic O-RAN framework, named DetO-RAN, which ensures deterministic transmission and computation for flows through a unified queuing and resource allocation mechanism. DetO-RAN introduces a three-queue model (i.e., wireless, wired, and computing queues) to support transmission and computation of flows, and establishes a closed-loop resource allocation process based on model training, inference, and updating. Based on this framework, we formulate a multi-objective optimization problem aiming to maximize the flow scheduling success rate and resource utilization. Due to the time-varying wireless channels, strongly coupled resources, and network dynamics, traditional optimization algorithms are inefficient. Thus, we decouple the problem into a radio resource block (RB) allocation subproblem as well as a time slot and computing resource allocation subproblem. Furthermore, we propose a meta-learning-based dual-timescale resource allocation (ML-DTRA) algorithm to solve them. Specifically, ML-DTRA performs RB allocation via a graph neural network at a small timescale and makes time slot and computing resource allocation decisions via deep reinforcement learning at a large timescale. Extensive simulation results demonstrate that the ML-DTRA algorithm significantly improves the scheduling success rate by 12.7% and the resource utilization by 15.9% compared with state-of-the-art benchmarks, showing its effectiveness in supporting end-to-end deterministic transmission and computation while improving resource efficiency in dynamic O-RAN environments.