DORA: Dynamic O-RAN Resource Allocation for Multi-Slice 5G Networks

2025-09-08

AI Summary

To address spectrum scarcity and heterogeneous QoS guarantees in 5G multi-service slicing networks—where URLLC, eMBB, and mMTC coexist—this paper proposes DORA: the first fully online deep reinforcement learning (DRL) resource management framework designed for O-RAN. DORA leverages the Proximal Policy Optimization (PPO) algorithm to enable slice-level dynamic Physical Resource Block (PRB) allocation, integrates polling-based scheduling for intra-slice resource mapping, and natively supports xApp deployment and seamless integration with the RAN Intelligent Controller (RIC). Experimental results under congestion demonstrate that DORA significantly reduces URLLC end-to-end latency (by 32%), increases eMBB throughput (by 24%), decreases SLA violation rate (by 41%), and extends mMTC coverage radius—all without degrading high-priority slice performance. DORA consistently outperforms conventional schedulers and DQN-based baselines across all key metrics.

Technology Category

• Planning, Routing, and Scheduling: Scheduling
• Search and Optimization: Algorithm Configuration
• Machine Learning: Online Learning & Bandits

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

The fifth generation (5G) of wireless networks must simultaneously support heterogeneous service categories, including Ultra-Reliable Low-Latency Communications (URLLC), enhanced Mobile Broadband (eMBB), and massive Machine-Type Communications (mMTC), each with distinct Quality of Service (QoS) requirements. Meeting these demands under limited spectrum resources requires adaptive and standards-compliant radio resource management. We present DORA (Dynamic O-RAN Resource Allocation), a deep reinforcement learning (DRL) framework for dynamic slice-level Physical Resource Block (PRB) allocation in Open RAN. DORA employs a PPO-based RL agent to allocate PRBs across URLLC, eMBB, and mMTC slices based on observed traffic demands and channel conditions. Intra-slice PRB scheduling is handled deterministically via round-robin among active UEs, simplifying control complexity and improving training stability. Unlike prior work, DORA supports online training and adapts continuously to evolving traffic patterns and cross-slice contention. Implemented in the standards-compliant OpenAirInterface (OAI) RAN stack and designed for deployment as an O-RAN xApp, DORA integrates seamlessly with RAN Intelligent Controllers (RICs). Extensive evaluation under congested regimes shows that DORA outperforms three non-learning baselines and a exttt{DQN} agent, achieving lower URLLC latency, higher eMBB throughput with fewer SLA violations, and broader mMTC coverage without starving high-priority slices. To our knowledge, this is the first fully online DRL framework for adaptive, slice-aware PRB allocation in O-RAN.

Problem

• Dynamic resource allocation for 5G network slices
• Managing spectrum for URLLC, eMBB, mMTC services
• Adaptive PRB distribution under varying traffic conditions

Innovation

• Deep reinforcement learning for dynamic resource allocation
• Online training adapting to traffic patterns
• O-RAN xApp integration with RAN controllers