Reinforcement learning-based channel access mechanism for multi-base station slotted Aloha with cooperative reception
With the increasingly dense deployment of base stations in the internet of things (IoT), the importance of interference management becomes ever more pronounced. In IoT environments, devices often employ random access, connecting to channels in a distributed manner. In scenarios involving massive num...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
China InfoCom Media Group
2024-06-01
|
| Series: | 物联网学报 |
| Subjects: | |
| Online Access: | http://www.wlwxb.com.cn/zh/article/doi/10.11959/j.issn.2096-3750.2024.00388/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841533818489536512 |
|---|---|
| author | HUANG Yuankang ZHAN Wen SUN Xinghua |
| author_facet | HUANG Yuankang ZHAN Wen SUN Xinghua |
| author_sort | HUANG Yuankang |
| collection | DOAJ |
| description | With the increasingly dense deployment of base stations in the internet of things (IoT), the importance of interference management becomes ever more pronounced. In IoT environments, devices often employ random access, connecting to channels in a distributed manner. In scenarios involving massive numbers of devices, severe interference may arise between nodes, leading to significant degradation in the throughput performance of the network. To address interference control issues in networks with random access, a multi-base station slotted Aloha network based on cooperative reception was considered, the reinforcement learning techniques was leveraged to design adaptive transmission algorithms that effectively managed interference, optimized network throughput performance, and enhanced network fairness. Firstly, an adaptive transmission algorithm were devised based on Q-learning, which was verified to maintain high network throughput performance under varying traffic conditions through simulation. Secondly, to improve network fairness, the penalty function method was employed to refine the adaptive transmission algorithm. Simulations confirm that the fairness-optimized algorithm significantly enhances network fairness while preserving satisfactory network throughput performance. |
| format | Article |
| id | doaj-art-6a54d176cd3c4057927d2e1f53112e1e |
| institution | Kabale University |
| issn | 2096-3750 |
| language | zho |
| publishDate | 2024-06-01 |
| publisher | China InfoCom Media Group |
| record_format | Article |
| series | 物联网学报 |
| spelling | doaj-art-6a54d176cd3c4057927d2e1f53112e1e2025-01-15T02:54:09ZzhoChina InfoCom Media Group物联网学报2096-37502024-06-018263567576803Reinforcement learning-based channel access mechanism for multi-base station slotted Aloha with cooperative receptionHUANG YuankangZHAN WenSUN XinghuaWith the increasingly dense deployment of base stations in the internet of things (IoT), the importance of interference management becomes ever more pronounced. In IoT environments, devices often employ random access, connecting to channels in a distributed manner. In scenarios involving massive numbers of devices, severe interference may arise between nodes, leading to significant degradation in the throughput performance of the network. To address interference control issues in networks with random access, a multi-base station slotted Aloha network based on cooperative reception was considered, the reinforcement learning techniques was leveraged to design adaptive transmission algorithms that effectively managed interference, optimized network throughput performance, and enhanced network fairness. Firstly, an adaptive transmission algorithm were devised based on Q-learning, which was verified to maintain high network throughput performance under varying traffic conditions through simulation. Secondly, to improve network fairness, the penalty function method was employed to refine the adaptive transmission algorithm. Simulations confirm that the fairness-optimized algorithm significantly enhances network fairness while preserving satisfactory network throughput performance.http://www.wlwxb.com.cn/zh/article/doi/10.11959/j.issn.2096-3750.2024.00388/reinforcement learninginternet of thingsrandom accessmulti-base station networkslotted Aloha |
| spellingShingle | HUANG Yuankang ZHAN Wen SUN Xinghua Reinforcement learning-based channel access mechanism for multi-base station slotted Aloha with cooperative reception 物联网学报 reinforcement learning internet of things random access multi-base station network slotted Aloha |
| title | Reinforcement learning-based channel access mechanism for multi-base station slotted Aloha with cooperative reception |
| title_full | Reinforcement learning-based channel access mechanism for multi-base station slotted Aloha with cooperative reception |
| title_fullStr | Reinforcement learning-based channel access mechanism for multi-base station slotted Aloha with cooperative reception |
| title_full_unstemmed | Reinforcement learning-based channel access mechanism for multi-base station slotted Aloha with cooperative reception |
| title_short | Reinforcement learning-based channel access mechanism for multi-base station slotted Aloha with cooperative reception |
| title_sort | reinforcement learning based channel access mechanism for multi base station slotted aloha with cooperative reception |
| topic | reinforcement learning internet of things random access multi-base station network slotted Aloha |
| url | http://www.wlwxb.com.cn/zh/article/doi/10.11959/j.issn.2096-3750.2024.00388/ |
| work_keys_str_mv | AT huangyuankang reinforcementlearningbasedchannelaccessmechanismformultibasestationslottedalohawithcooperativereception AT zhanwen reinforcementlearningbasedchannelaccessmechanismformultibasestationslottedalohawithcooperativereception AT sunxinghua reinforcementlearningbasedchannelaccessmechanismformultibasestationslottedalohawithcooperativereception |