AIIM learns coordinated PRB allocation policies in a hybrid full-stack O-RAN testbed to improve QoS satisfaction and reduce interference-induced PRB loss compared to proportional-fair baselines while preserving aggregate throughput.
DORA: Dynamic O- RAN resource allocation for multi-slice 5G networks
3 Pith papers cite this work. Polarity classification is still indexing.
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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 \texttt{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.
years
2026 3representative citing papers
SVD projection onto a clean-KPI subspace recovers 100% DRL return against four O-RAN backdoor attacks whenever trigger energy lies in the orthogonal complement.
MORPH fuses iPerf measurements on OpenAirInterface, MCS-conditioned theoretical throughput, and 3GPP PHY simulation to train RL agents that achieve more robust slice performance and SLA compliance than single-source training for PRB-level spectrum allocation in a single gNB.
citing papers explorer
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AIIM: Adaptive Inter-cell Interference Mitigation for Heterogeneous Multi-vendor 5G O-RAN Networks
AIIM learns coordinated PRB allocation policies in a hybrid full-stack O-RAN testbed to improve QoS satisfaction and reduce interference-induced PRB loss compared to proportional-fair baselines while preserving aggregate throughput.
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ORAN-DEFEND: Subspace Detection and Sanitization of Backdoor DRL xApps in Open RAN
SVD projection onto a clean-KPI subspace recovers 100% DRL return against four O-RAN backdoor attacks whenever trigger energy lies in the orthogonal complement.
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MORPH: Multi-Environment Orchestrated Reinforcement Learning for PRB Handling in O-RAN
MORPH fuses iPerf measurements on OpenAirInterface, MCS-conditioned theoretical throughput, and 3GPP PHY simulation to train RL agents that achieve more robust slice performance and SLA compliance than single-source training for PRB-level spectrum allocation in a single gNB.