Spectrum Sharing between Cellular and Wi-Fi Networks based on Deep Reinforcement Learning
書誌事項
- 公開日
- 2023-01-30
- 資源種別
- journal article
- DOI
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- 10.5121/ijcnc.2023.15108
- 公開者
- Academy and Industry Research Collaboration Center (AIRCC)
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説明
<jats:p>Recently, mobile traffic is growing rapidly and spectrum resources are becoming scarce in wireless networks. Due to this, the wireless network capacity will not meet the traffic demand. To address this problem, using cellular systems in an unlicensed spectrum emerged as an effective solution. In this case, cellular systems need to coexist with Wi-Fi and other systems. For that, we propose an efficient channel assignment method for Wi-Fi AP and cellular NB, based on the DRL method. To train the DDQN model, we implement an emulator as an environment for spectrum sharing in densely deployed NB and APs in wireless heterogeneous networks. Our proposed DDQN algorithm improves the average throughput from 25.5% to 48.7% in different user arrival rates compared to the conventional method. We evaluated the generalization performance of the trained agent, to confirm channel allocation efficiency in terms of average throughput under the different user arrival rates.</jats:p>
収録刊行物
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- International journal of Computer Networks & Communications
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International journal of Computer Networks & Communications 15 (01), 123-143, 2023-01-30
Academy and Industry Research Collaboration Center (AIRCC)
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詳細情報 詳細情報について
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- CRID
- 1360580229821687552
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- ISSN
- 09752293
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- 資料種別
- journal article
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- データソース種別
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- Crossref
- KAKEN
- OpenAIRE