Resource allocation strategy based on deep reinforcement learning in 6G dense network
In order to realize no overlapping interference between cells, 6G dense network (DN) adopting resource allocation is the important technology of enhancing network performance.However, limited resources and dense distribution of nodes make it difficult to solve the problem of resource allocation thro...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Journal on Communications
2023-08-01
|
| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2023148/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841540064879837184 |
|---|---|
| author | Fan YANG Cheng YANG Jie HUANG Shilong ZHANG Tao YU Xun ZUO Chuan YANG |
| author_facet | Fan YANG Cheng YANG Jie HUANG Shilong ZHANG Tao YU Xun ZUO Chuan YANG |
| author_sort | Fan YANG |
| collection | DOAJ |
| description | In order to realize no overlapping interference between cells, 6G dense network (DN) adopting resource allocation is the important technology of enhancing network performance.However, limited resources and dense distribution of nodes make it difficult to solve the problem of resource allocation through traditional optimization methods.To tackle the problem, a point-line graph coloring based overlapping interference model was formulated and a Dueling deep Q-network (DQN) based resource allocation method was proposed, which combined deep reinforcement learning (DRL) and the overlapping interference model.Specifically, the proposed method adopted the overlapping interference model and resource reuse rate to design the immediate reward.Then, generating 6G DN resource allocation strategies were independently learned by using Dueling DQN to achieve the goal of realizing resource allocation without overlapping interference between cells.The performance evaluation results show that the proposed method can effectively increase both network throughput and resource reuse rate, as well as enhance network performance. |
| format | Article |
| id | doaj-art-b1bc4dcce2de48c791f4e3c80f32c481 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2023-08-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-b1bc4dcce2de48c791f4e3c80f32c4812025-01-14T06:22:52ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2023-08-014421522759386127Resource allocation strategy based on deep reinforcement learning in 6G dense networkFan YANGCheng YANGJie HUANGShilong ZHANGTao YUXun ZUOChuan YANGIn order to realize no overlapping interference between cells, 6G dense network (DN) adopting resource allocation is the important technology of enhancing network performance.However, limited resources and dense distribution of nodes make it difficult to solve the problem of resource allocation through traditional optimization methods.To tackle the problem, a point-line graph coloring based overlapping interference model was formulated and a Dueling deep Q-network (DQN) based resource allocation method was proposed, which combined deep reinforcement learning (DRL) and the overlapping interference model.Specifically, the proposed method adopted the overlapping interference model and resource reuse rate to design the immediate reward.Then, generating 6G DN resource allocation strategies were independently learned by using Dueling DQN to achieve the goal of realizing resource allocation without overlapping interference between cells.The performance evaluation results show that the proposed method can effectively increase both network throughput and resource reuse rate, as well as enhance network performance.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2023148/6G dense networkoverlapping interferencedeep Q-networkresource allocation |
| spellingShingle | Fan YANG Cheng YANG Jie HUANG Shilong ZHANG Tao YU Xun ZUO Chuan YANG Resource allocation strategy based on deep reinforcement learning in 6G dense network Tongxin xuebao 6G dense network overlapping interference deep Q-network resource allocation |
| title | Resource allocation strategy based on deep reinforcement learning in 6G dense network |
| title_full | Resource allocation strategy based on deep reinforcement learning in 6G dense network |
| title_fullStr | Resource allocation strategy based on deep reinforcement learning in 6G dense network |
| title_full_unstemmed | Resource allocation strategy based on deep reinforcement learning in 6G dense network |
| title_short | Resource allocation strategy based on deep reinforcement learning in 6G dense network |
| title_sort | resource allocation strategy based on deep reinforcement learning in 6g dense network |
| topic | 6G dense network overlapping interference deep Q-network resource allocation |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2023148/ |
| work_keys_str_mv | AT fanyang resourceallocationstrategybasedondeepreinforcementlearningin6gdensenetwork AT chengyang resourceallocationstrategybasedondeepreinforcementlearningin6gdensenetwork AT jiehuang resourceallocationstrategybasedondeepreinforcementlearningin6gdensenetwork AT shilongzhang resourceallocationstrategybasedondeepreinforcementlearningin6gdensenetwork AT taoyu resourceallocationstrategybasedondeepreinforcementlearningin6gdensenetwork AT xunzuo resourceallocationstrategybasedondeepreinforcementlearningin6gdensenetwork AT chuanyang resourceallocationstrategybasedondeepreinforcementlearningin6gdensenetwork |