mlsanityData to 14 Oct 2026

Generalizable Resource Scaling of 5G Slices using Constrained Reinforcement Learning

arXiv 2306.09290 ↗First posted 15 Jun 2023cs.NI cs.LGcited by 4

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Abstract (arXiv)

Network slicing is a key enabler for 5G to support various applications. Slices requested by service providers (SPs) have heterogeneous quality of service (QoS) requirements, such as latency, throughput, and jitter. It is imperative that the 5G infrastructure provider (InP) allocates the right amount of resources depending on the slice's traffic, such that the specified QoS levels are maintained during the slice's lifetime while maximizing resource efficiency. However, there is a non-trivial relationship between the QoS and resource allocation. In this paper, this relationship is learned using a regression-based model. We also leverage a risk-constrained reinforcement learning agent that is trained offline using this model and domain randomization for dynamically scaling slice resources while maintaining the desired QoS level. Our novel approach reduces the effects of network modeling errors since it is model-free and does not require QoS metrics to be mathematically formulated in terms of traffic. In addition, it provides robustness against uncertain network conditions, generalizes to different real-world traffic patterns, and caters to various QoS metrics. The results show that the state-of-the-art approaches can lead to QoS degradation as high as 44.5% when tested on previously unseen traffic. On the other hand, our approach maintains the QoS degradation below a preset 10% threshold on such traffic, while minimizing the allocated resources. Additionally, we demonstrate that the proposed approach is robust against varying network conditions and inaccurate traffic predictions.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 12 of its 23 references that are in the map. First posted on arXiv 15 Jun 2023.

Builds on

12 of 23 references.

References · 12

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2015Trust Region Policy Optimizationcited by 2,580
  3. 2017Constrained Policy Optimizationcited by 983
  4. 2018Reward Constrained Policy Optimizationcited by 417
  5. 2016Learning Representations for Counterfactual Inferencecited by 329
  6. 2020Learning to Walk in the Real World with Minimal Human Effortcited by 111
  7. 2022Trustworthy Reinforcement Learning Against Intrinsic Vulnerabilities: Robustness, Safety, and Generalizabilitycited by 36
  8. 2018Deep Reinforcement Learning for Resource Management in Network Slicingcited by 27
  9. 2021NeuralProphet: Explainable Forecasting at Scalecited by 22
  10. 2021OnSlicing: Online End-to-End Network Slicing with Reinforcement Learningcited by 7
  11. 2021Constraint-Aware Deep Reinforcement Learning for End-to-End Resource Orchestration in Mobile Networkscited by 6
  12. 2018Stochastic Optimization and Control Framework for 5G Network Slicing with Effective Isolationcited by 2

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Cited by year

Citing papers per year: 2025: 4.

4 papers.

  1. 2025SafeSlice: Enabling SLA-Compliant O-RAN Slicing via Safe Deep Reinforcement Learningcited by 1
  2. 2025A Hybrid Reinforcement Learning Framework for Hard Latency Constrained Resource Schedulingcited by 1
  3. 2025Context-aware Constrained Reinforcement Learning Based Energy-Efficient Power Scheduling for Non-stationary XR Data Trafficcited by 0
  4. 2025Joint User Priority and Power Scheduling for QoS-Aware WMMSE Precoding: A Constrained-Actor Attentive-Critic Approachcited by 0