mlsanityData to 14 Oct 2026

Self-play Learning Strategies for Resource Assignment in Open-RAN Networks

arXiv 2103.02649 ↗First posted 3 Mar 2021cs.NI cs.AIcited by 8

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Abstract (arXiv)

Open Radio Access Network (ORAN) is being developed with an aim to democratise access and lower the cost of future mobile data networks, supporting network services with various QoS requirements, such as massive IoT and URLLC. In ORAN, network functionality is dis-aggregated into remote units (RUs), distributed units (DUs) and central units (CUs), which allows flexible software on Commercial-Off-The-Shelf (COTS) deployments. Furthermore, the mapping of variable RU requirements to local mobile edge computing centres for future centralized processing would significantly reduce the power consumption in cellular networks. In this paper, we study the RU-DU resource assignment problem in an ORAN system, modelled as a 2D bin packing problem. A deep reinforcement learning-based self-play approach is proposed to achieve efficient RU-DU resource management, with AlphaGo Zero inspired neural Monte-Carlo Tree Search (MCTS). Experiments on representative 2D bin packing environment and real sites data show that the self-play learning strategy achieves intelligent RU-DU resource assignment for different network conditions.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 13 of its 44 references that are in the map. First posted on arXiv 3 Mar 2021.

Builds on

13 of 44 references.

References · 13

  1. 2018IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architecturescited by 883
  2. 2017Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithmcited by 734
  3. 2018Machine Learning for Combinatorial Optimization: a Methodological Tour d'Horizoncited by 623
  4. 2017Thinking Fast and Slow with Deep Learning and Tree Searchcited by 252
  5. 2020Reinforcement Learning for Combinatorial Optimization: A Surveycited by 236
  6. 2018Applications of Deep Reinforcement Learning in Communications and Networking: A Surveycited by 167
  7. 2018Ranked Reward: Enabling Self-Play Reinforcement Learning for Combinatorial Optimizationcited by 27
  8. 2019Machine Learning for Resource Management in Cellular and IoT Networks: Potentials, Current Solutions, and Open Challengescited by 19
  9. 2019Learning Self-Game-Play Agents for Combinatorial Optimization Problemscited by 7
  10. 2019Deep Reinforcement Learning with Discrete Normalized Advantage Functions for Resource Management in Network Slicingcited by 6
  11. 2020Tackling Morpion Solitaire with AlphaZero-likeRanked Reward Reinforcement Learningcited by 6
  12. 2018A Multi-task Selected Learning Approach for Solving 3D Flexible Bin Packing Problemcited by 4
1 more
  1. 2019Resource Allocation for Secure URLLC in Mission-Critical IoT Scenariocited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 2; 2022: 1; 2023: 2; 2024: 1; 2025: 1; 2026: 1.

8 papers.

  1. 2023Beyond Games: A Systematic Review of Neural Monte Carlo Tree Search Applicationscited by 16
  2. 2021RLOps: Development Life-cycle of Reinforcement Learning Aided Open RANcited by 15
  3. 2021Attend2Pack: Bin Packing through Deep Reinforcement Learning with Attentioncited by 13
  4. 2026ML-Enabled Open RAN: A Comprehensive Survey of Architectures, Challenges, and Opportunitiescited by 4
  5. 2022Intelligent Closed-loop RAN Control with xApps in OpenRAN Gymcited by 2
  6. 2023Fairness Guaranteed and Auction-based x-haul and Cloud Resource Allocation in Multi-tenant O-RANscited by 1
  7. 2024Generative AI for O-RAN Slicing: A Semi-Supervised Approach with VAE and Contrastive Learningcited by 1
  8. 2025Optimizing 2D+1 Packing in Constrained Environments Using Deep Reinforcement Learningcited by 0