Joint Channel Allocation and Power Control Based on Long Short-Term Memory Deep Q Network in Cognitive Radio Networks
Efficient spectrum resource management in cognitive radio networks (CRNs) is a promising method that improves the utilization of spectrum resource. In particular, the power control and channel allocation are of top priorities in spectrum resource management. Nevertheless, the joint design of power c...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/1628023 |
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| author | Zifeng Ye Yonghua Wang Pin Wan |
| author_facet | Zifeng Ye Yonghua Wang Pin Wan |
| author_sort | Zifeng Ye |
| collection | DOAJ |
| description | Efficient spectrum resource management in cognitive radio networks (CRNs) is a promising method that improves the utilization of spectrum resource. In particular, the power control and channel allocation are of top priorities in spectrum resource management. Nevertheless, the joint design of power control and channel allocation is an NP-hard problem and the research is still in the preliminary stage. In this paper, we propose a novel joint approach based on long short-term memory deep Q network (LSTM-DQN). Our objective is to obtain the channel allocation schemes of the access points (APs) and the power control strategies of the secondary users (SUs). Specifically, the received signal strength information (RSSI) collected by the microbase stations is used as the input of LSTM-DQN. In this way, the collection of RSSI can be shared between users. After the training is completed, the APs are capable of selecting channels with small interference while the SUs may access the authorized channels in an underlay operation mode without knowing any knowledge about the primary users (PUs). Experimental results show that the channels are allocated to the APs with a lower probability of collision. Moreover, the SUs can adjust their power control strategies quickly to avoid the harmful interference to the PUs when the environment parameters change randomly. Consequently, the overall performance of CRNs and the utilization of spectrum resources are improved significantly compared to existing popular solutions. |
| format | Article |
| id | doaj-art-047e4a8e94374a79a7cd6541e398c53b |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-047e4a8e94374a79a7cd6541e398c53b2025-02-03T05:53:56ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/16280231628023Joint Channel Allocation and Power Control Based on Long Short-Term Memory Deep Q Network in Cognitive Radio NetworksZifeng Ye0Yonghua Wang1Pin Wan2School of Automation, Guangdong University of Technology, Guangzhou 510006, ChinaSchool of Automation, Guangdong University of Technology, Guangzhou 510006, ChinaSchool of Automation, Guangdong University of Technology, Guangzhou 510006, ChinaEfficient spectrum resource management in cognitive radio networks (CRNs) is a promising method that improves the utilization of spectrum resource. In particular, the power control and channel allocation are of top priorities in spectrum resource management. Nevertheless, the joint design of power control and channel allocation is an NP-hard problem and the research is still in the preliminary stage. In this paper, we propose a novel joint approach based on long short-term memory deep Q network (LSTM-DQN). Our objective is to obtain the channel allocation schemes of the access points (APs) and the power control strategies of the secondary users (SUs). Specifically, the received signal strength information (RSSI) collected by the microbase stations is used as the input of LSTM-DQN. In this way, the collection of RSSI can be shared between users. After the training is completed, the APs are capable of selecting channels with small interference while the SUs may access the authorized channels in an underlay operation mode without knowing any knowledge about the primary users (PUs). Experimental results show that the channels are allocated to the APs with a lower probability of collision. Moreover, the SUs can adjust their power control strategies quickly to avoid the harmful interference to the PUs when the environment parameters change randomly. Consequently, the overall performance of CRNs and the utilization of spectrum resources are improved significantly compared to existing popular solutions.http://dx.doi.org/10.1155/2020/1628023 |
| spellingShingle | Zifeng Ye Yonghua Wang Pin Wan Joint Channel Allocation and Power Control Based on Long Short-Term Memory Deep Q Network in Cognitive Radio Networks Complexity |
| title | Joint Channel Allocation and Power Control Based on Long Short-Term Memory Deep Q Network in Cognitive Radio Networks |
| title_full | Joint Channel Allocation and Power Control Based on Long Short-Term Memory Deep Q Network in Cognitive Radio Networks |
| title_fullStr | Joint Channel Allocation and Power Control Based on Long Short-Term Memory Deep Q Network in Cognitive Radio Networks |
| title_full_unstemmed | Joint Channel Allocation and Power Control Based on Long Short-Term Memory Deep Q Network in Cognitive Radio Networks |
| title_short | Joint Channel Allocation and Power Control Based on Long Short-Term Memory Deep Q Network in Cognitive Radio Networks |
| title_sort | joint channel allocation and power control based on long short term memory deep q network in cognitive radio networks |
| url | http://dx.doi.org/10.1155/2020/1628023 |
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