# Reinforcement learning for Admission Control in 5G Wireless Networks

arXiv: https://arxiv.org/abs/2104.10761 · HTML: https://mlsanity.com/paper/2104.10761/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 24 references that are in the map.
- First posted on arXiv 13 Apr 2021.

## Builds on (most cited first, 1 of 1)

- [Tenant-Aware Slice Admission Control using Neural Networks-Based Policy Agent](https://mlsanity.com/paper/1908.07494/) (2019)

## Built on by (most cited first, 1 of 1)

- [Reliability-Optimized User Admission Control for URLLC Traffic: A Neural Contextual Bandit Approach](https://mlsanity.com/paper/2401.03059/) (2024)

## Abstract (arXiv)

The key challenge in admission control in wireless networks is to strike an optimal trade-off between the blocking probability for new requests while minimizing the dropping probability of ongoing requests. We consider two approaches for solving the admission control problem: i) the typically adopted threshold policy and ii) our proposed policy relying on reinforcement learning with neural networks. Extensive simulation experiments are conducted to analyze the performance of both policies. The results show that the reinforcement learning policy outperforms the threshold-based policies in the scenario with heterogeneous time-varying arrival rates and multiple user equipment types, proving its applicability in realistic wireless network scenarios.