mlsanityData to 14 Oct 2026

Evolutionary Deep Reinforcement Learning for Dynamic Slice Management in O-RAN

arXiv 2208.14394 ↗First posted 30 Aug 2022eess.SY cs.AI cs.ITcited by 11

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Abstract (arXiv)

The next-generation wireless networks are required to satisfy a variety of services and criteria concurrently. To address upcoming strict criteria, a new open radio access network (O-RAN) with distinguishing features such as flexible design, disaggregated virtual and programmable components, and intelligent closed-loop control was developed. O-RAN slicing is being investigated as a critical strategy for ensuring network quality of service (QoS) in the face of changing circumstances. However, distinct network slices must be dynamically controlled to avoid service level agreement (SLA) variation caused by rapid changes in the environment. Therefore, this paper introduces a novel framework able to manage the network slices through provisioned resources intelligently. Due to diverse heterogeneous environments, intelligent machine learning approaches require sufficient exploration to handle the harshest situations in a wireless network and accelerate convergence. To solve this problem, a new solution is proposed based on evolutionary-based deep reinforcement learning (EDRL) to accelerate and optimize the slice management learning process in the radio access network's (RAN) intelligent controller (RIC) modules. To this end, the O-RAN slicing is represented as a Markov decision process (MDP) which is then solved optimally for resource allocation to meet service demand using the EDRL approach. In terms of reaching service demands, simulation results show that the proposed approach outperforms the DRL baseline by 62.2%.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 9 of its 18 references that are in the map. First posted on arXiv 30 Aug 2022.

Builds on

9 of 18 references.

References · 9

  1. 2018Evolution-Guided Policy Gradient in Reinforcement Learningcited by 83
  2. 2022Understanding O-RAN: Architecture, Interfaces, Algorithms, Security, and Research Challengescited by 55
  3. 2020Intelligence and Learning in O-RAN for Data-driven NextG Cellular Networkscited by 45
  4. 2017Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learningcited by 40
  5. 2021ColO-RAN: Developing Machine Learning-based xApps for Open RAN Closed-loop Control on Programmable Experimental Platformscited by 35
  6. 2020Intelligent O-RAN for Beyond 5G and 6G Wireless Networkscited by 14
  7. 2022Predictive Closed-Loop Service Automation in O-RAN based Network Slicingcited by 8
  8. 2021Semantic-Aware Collaborative Deep Reinforcement Learning Over Wireless Cellular Networkscited by 6
  9. 2021Performance Analysis and Optimization of Uplink Cellular Networks with Flexible Frame Structurecited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 4; 2024: 2; 2025: 4; 2026: 1.

11 papers.

  1. 2024Meta Reinforcement Learning Approach for Adaptive Resource Optimization in O-RANcited by 8
  2. 2023Attention-based Open RAN Slice Management using Deep Reinforcement Learningcited by 7
  3. 2023Safe and Accelerated Deep Reinforcement Learning-based O-RAN Slicing: A Hybrid Transfer Learning Approachcited by 5
  4. 2024Open RAN LSTM Traffic Prediction and Slice Management using Deep Reinforcement Learningcited by 5
  5. 2026ML-Enabled Open RAN: A Comprehensive Survey of Architectures, Challenges, and Opportunitiescited by 4
  6. 2023Triplet Loss-less Center Loss Sampling Strategies in Facial Expression Recognition Scenarioscited by 3
  7. 2025ORAN-GUIDE: RAG-Driven Prompt Learning for LLM-Augmented Reinforcement Learning in O-RAN Network Slicingcited by 3
  8. 2023Joint Path planning and Power Allocation of a Cellular-Connected UAV using Apprenticeship Learning via Deep Inverse Reinforcement Learningcited by 2
  9. 2025Task Specific Sharpness Aware O-RAN Resource Management using Multi Agent Reinforcement Learningcited by 1
  10. 2025Federated Neuroevolution O-RAN: Enhancing the Robustness of Deep Reinforcement Learning xAppscited by 0
  11. 2025Meta Hierarchical Reinforcement Learning for Scalable Resource Management in O-RANcited by 0