Abstract
6G networks are expected to revolutionize connectivity, offering significant improvements in speed, capacity, and smart automation. However, existing network designs will struggle to handle the demands of 6G, which include much faster speeds, a huge increase in connected devices, lower energy consumption, extremely quick response times, and better mobile broadband. To solve this problem, incorporating the artificial intelligence (AI) technologies has been proposed. This idea led to the concept of Knowledge-Defined Networking (KDN). KDN promises many improvements, such as resource management, routing, scheduling, clustering, and mobility prediction. The main goal of this study is to optimize resource management using Reinforcement Learning.
| Original language | English |
|---|---|
| Pages (from-to) | 1455-1460 |
| Number of pages | 6 |
| Journal | International Conference on Computer Science and Engineering, UBMK |
| Issue number | 2025 |
| DOIs | |
| Publication status | Published - 2025 |
| Event | 10th International Conference on Computer Science and Engineering, UBMK 2025 - Istanbul, Turkey Duration: 17 Sept 2025 → 21 Sept 2025 |
Bibliographical note
Publisher Copyright:© 2025 IEEE.
Keywords
- 6G
- KDN
- machine learning
- reinforcement learning
- resource management
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