# Generalizable Resource Scaling of 5G Slices using Constrained Reinforcement Learning

arXiv: https://arxiv.org/abs/2306.09290 · HTML: https://mlsanity.com/paper/2306.09290/

## 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 (most cited first, 12 of 12)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Trust Region Policy Optimization](https://mlsanity.com/paper/1502.05477/) (2015)
- [Constrained Policy Optimization](https://mlsanity.com/paper/1705.10528/) (2017)
- [Reward Constrained Policy Optimization](https://mlsanity.com/paper/1805.11074/) (2018)
- [Learning Representations for Counterfactual Inference](https://mlsanity.com/paper/1605.03661/) (2016)
- [Learning to Walk in the Real World with Minimal Human Effort](https://mlsanity.com/paper/2002.08550/) (2020)
- [Trustworthy Reinforcement Learning Against Intrinsic Vulnerabilities: Robustness, Safety, and Generalizability](https://mlsanity.com/paper/2209.08025/) (2022)
- [Deep Reinforcement Learning for Resource Management in Network Slicing](https://mlsanity.com/paper/1805.06591/) (2018)
- [NeuralProphet: Explainable Forecasting at Scale](https://mlsanity.com/paper/2111.15397/) (2021)
- [OnSlicing: Online End-to-End Network Slicing with Reinforcement Learning](https://mlsanity.com/paper/2111.01616/) (2021)
- [Constraint-Aware Deep Reinforcement Learning for End-to-End Resource Orchestration in Mobile Networks](https://mlsanity.com/paper/2110.04320/) (2021)
- [Stochastic Optimization and Control Framework for 5G Network Slicing with Effective Isolation](https://mlsanity.com/paper/1801.10282/) (2018)

## Built on by (most cited first, 4 of 4)

- [SafeSlice: Enabling SLA-Compliant O-RAN Slicing via Safe Deep Reinforcement Learning](https://mlsanity.com/paper/2503.12753/) (2025)
- [A Hybrid Reinforcement Learning Framework for Hard Latency Constrained Resource Scheduling](https://mlsanity.com/paper/2504.03721/) (2025)
- [Context-aware Constrained Reinforcement Learning Based Energy-Efficient Power Scheduling for Non-stationary XR Data Traffic](https://mlsanity.com/paper/2503.09391/) (2025)
- [Joint User Priority and Power Scheduling for QoS-Aware WMMSE Precoding: A Constrained-Actor Attentive-Critic Approach](https://mlsanity.com/paper/2506.16074/) (2025)

## 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.