Knowledge Defined Networking for 6G: A Reinforcement Learning Example for Resource Management
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916979852443648 |
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| author | Koçoğlu, Erol Ozdem, Mehmet Bilen, Tuğçe |
| author_facet | Koçoğlu, Erol Ozdem, Mehmet Bilen, Tuğçe |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_26075 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Knowledge Defined Networking for 6G: A Reinforcement Learning Example for Resource Management Koçoğlu, Erol Ozdem, Mehmet Bilen, Tuğçe Networking and Internet Architecture 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. |
| title | Knowledge Defined Networking for 6G: A Reinforcement Learning Example for Resource Management |
| topic | Networking and Internet Architecture |
| url | https://arxiv.org/abs/2509.26075 |