# Subcarrier Assignment Schemes Based on Q-Learning in Wideband Cognitive Radio Networks

arXiv: https://arxiv.org/abs/1911.07149 · HTML: https://mlsanity.com/paper/1911.07149/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Nov 2019.

## Built on by (most cited first, 3 of 3)

- [When Machine Learning Meets Spectrum Sharing Security: Methodologies and Challenges](https://mlsanity.com/paper/2201.04677/) (2022)
- [Deep Learning Frameworks for Cognitive Radio Networks: Review and Open Research Challenges](https://mlsanity.com/paper/2410.23949/) (2024)
- [Learning for Cross-Layer Resource Allocation in MEC-Aided Cell-Free Networks](https://mlsanity.com/paper/2412.16565/) (2024)

## Abstract (arXiv)

Subcarrier assignment is of crucial importance in wideband cognitive radio (CR) networks. In order to tackle the challenge that the traditional optimization-based methods are inappropriate in the dynamic spectrum access environment, an independent Q-learning-based scheme is proposed for the case that the secondary users (SUs) cannot exchange information while a collaborative Q-learning-based scheme is proposed for the case that information can be exchange among SUs. Simulation results show that the performance achieved with the proposed collaborative Q-learning-based assignment is better than that obtained with the proposed independent Q-learning-based assignment at the cost of the computation cost.