# Deep Reinforcement Learning Based Resource Allocation for Cloud Native Wireless Network

arXiv: https://arxiv.org/abs/2305.06249 · HTML: https://mlsanity.com/paper/2305.06249/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 28 references that are in the map.
- First posted on arXiv 10 May 2023.

## Builds on (most cited first, 6 of 6)

- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [The Road Towards 6G: A Comprehensive Survey](https://mlsanity.com/paper/2102.01420/) (2021)
- [Resource Scheduling in Edge Computing: A Survey](https://mlsanity.com/paper/2108.08059/) (2021)
- [DeepSlicing: Deep Reinforcement Learning Assisted Resource Allocation for Network Slicing](https://mlsanity.com/paper/2008.07614/) (2020)
- [Resource Allocation for Edge Computing in IoT Networks via Reinforcement Learning](https://mlsanity.com/paper/1903.01856/) (2019)
- [Computation Offloading for IoT in C-RAN: Optimization and Deep Learning](https://mlsanity.com/paper/1909.10696/) (2019)

## Abstract (arXiv)

Cloud native technology has revolutionized 5G beyond and 6G communication networks, offering unprecedented levels of operational automation, flexibility, and adaptability. However, the vast array of cloud native services and applications presents a new challenge in resource allocation for dynamic cloud computing environments. To tackle this challenge, we investigate a cloud native wireless architecture that employs container-based virtualization to enable flexible service deployment. We then study two representative use cases: network slicing and Multi-Access Edge Computing. To optimize resource allocation in these scenarios, we leverage deep reinforcement learning techniques and introduce two model-free algorithms capable of monitoring the network state and dynamically training allocation policies. We validate the effectiveness of our algorithms in a testbed developed using Free5gc. Our findings demonstrate significant improvements in network efficiency, underscoring the potential of our proposed techniques in unlocking the full potential of cloud native wireless networks.