mlsanityData to 7 Oct 2026

Attention-based Open RAN Slice Management using Deep Reinforcement Learning

arXiv 2306.09490 ↗First posted 15 Jun 2023cs.DC cs.LG cs.NIcited by 7

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Abstract (arXiv)

As emerging networks such as Open Radio Access Networks (O-RAN) and 5G continue to grow, the demand for various services with different requirements is increasing. Network slicing has emerged as a potential solution to address the different service requirements. However, managing network slices while maintaining quality of services (QoS) in dynamic environments is a challenging task. Utilizing machine learning (ML) approaches for optimal control of dynamic networks can enhance network performance by preventing Service Level Agreement (SLA) violations. This is critical for dependable decision-making and satisfying the needs of emerging networks. Although RL-based control methods are effective for real-time monitoring and controlling network QoS, generalization is necessary to improve decision-making reliability. This paper introduces an innovative attention-based deep RL (ADRL) technique that leverages the O-RAN disaggregated modules and distributed agent cooperation to achieve better performance through effective information extraction and implementing generalization. The proposed method introduces a value-attention network between distributed agents to enable reliable and optimal decision-making. Simulation results demonstrate significant improvements in network performance compared to other DRL baseline methods.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 11 of its 21 references that are in the map. First posted on arXiv 15 Jun 2023.

Builds on

11 of 21 references.

References · 11

  1. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
  2. 2017Counterfactual Multi-Agent Policy Gradientscited by 733
  3. 2018Actor-Attention-Critic for Multi-Agent Reinforcement Learningcited by 259
  4. 2022Understanding O-RAN: Architecture, Interfaces, Algorithms, Security, and Research Challengescited by 54
  5. 2021ColO-RAN: Developing Machine Learning-based xApps for Open RAN Closed-loop Control on Programmable Experimental Platformscited by 33
  6. 20185G Wireless Network Slicing for eMBB, URLLC, and mMTC: A Communication-Theoretic Viewcited by 17
  7. 2022Federated Deep Reinforcement Learning for Resource Allocation in O-RAN Slicingcited by 14
  8. 2022Team Learning-Based Resource Allocation for Open Radio Access Network (O-RAN)cited by 11
  9. 2022Evolutionary Deep Reinforcement Learning for Dynamic Slice Management in O-RANcited by 11
  10. 2022Predictive Closed-Loop Service Automation in O-RAN based Network Slicingcited by 8
  11. 2022Reinforcement Learning Based Resource Allocation for Network Slices in O-RAN Midhaulcited by 4

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Cited by year

Citing papers per year: 2024: 3; 2025: 4.

7 papers.

  1. 2024Meta Reinforcement Learning Approach for Adaptive Resource Optimization in O-RANcited by 8
  2. 2024Open RAN LSTM Traffic Prediction and Slice Management using Deep Reinforcement Learningcited by 5
  3. 2025Intelligent Task Offloading: Advanced MEC Task Offloading and Resource Management in 5G Networkscited by 3
  4. 2025ORAN-GUIDE: RAG-Driven Prompt Learning for LLM-Augmented Reinforcement Learning in O-RAN Network Slicingcited by 3
  5. 2024FlameFinder: Illuminating Obscured Fire through Smoke with Attentive Deep Metric Learningcited by 2
  6. 2025Task Specific Sharpness Aware O-RAN Resource Management using Multi Agent Reinforcement Learningcited by 1
  7. 2025Meta Hierarchical Reinforcement Learning for Scalable Resource Management in O-RANcited by 0