研究生: |
于一方 Yu, Yi-Fang |
---|---|
論文名稱: |
使用強化學習的基於效用的資源分配以在異質網路中提供公平性和使用者滿意度 A Utility-based Resource Allocation Using Reinforce Learning to provide Fairness and User Satisfaction in Heterogeneous Network |
指導教授: |
王嘉斌
Wang, Chia-Pin |
口試委員: |
王嘉斌
Wang, Chia-Pin 郭文興 Kuo, Wen-Hsing 方士豪 Fang, Shih-Hau |
口試日期: | 2023/10/27 |
學位類別: |
碩士 Master |
系所名稱: |
電機工程學系 Department of Electrical Engineering |
論文出版年: | 2024 |
畢業學年度: | 112 |
語文別: | 中文 |
論文頁數: | 68 |
中文關鍵詞: | 微小型基地台 、未許可頻段 、IEEE 802.11 、長期演進 、強化式學習 |
英文關鍵詞: | Small cell, Unlicensed band, IEEE 802.11, Reinforcement learning, LTE |
研究方法: | 行動研究法 |
DOI URL: | http://doi.org/10.6345/NTNU202400134 |
論文種類: | 學術論文 |
相關次數: | 點閱:81 下載:2 |
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在下一代異構網絡中,5G NR基站將與Wi-Fi接入點競爭使用未許可的頻段,以提高傳輸速率。在3GPP LAA標準中,引入了一種名為LBT的技術,用於NR基站和Wi-Fi接入點的共存。然而,在LAA和Wi-Fi之間爭奪未許可頻段時,LAA中使用的LBT使競爭相對不公平;使用LBT的LAA的吞吐量遠高於Wi-Fi中使用的CSMA/CA。當Wi-Fi在與LAA共存時的性能主要取決於LAA如何配置LBT參數時,我們在本文中提出了一種基於強化學習的新型無線資源分配方案,該方案調整LAA的TXOP持續時間,以改善LAA和Wi-Fi之間的公平性。為了實現用戶公平性和系統吞吐量之間的均衡,這兩者通常是相互衝突且難以同時改善的,我們提出的基於RL的TXOP調整方案包括兩種策略:絕對公平和中度公平。如果最關心用戶的公平性,則可以利用提出的絕對公平方案以提供最佳的公平性性能。否則,如果需要兼顧公平性和吞吐量,則可以採用提出的中度公平方案以實現用戶公平性和系統吞吐量之間的平衡。
此外,我們還引入了一種基於效用的公平方案,可以根據效用函數來保證用戶感知的服務質量(QoS)。模擬結果表明,所提出的方案能夠有效改善LAA和Wi-Fi之間的公平性,並同時考慮根據不同的網絡策略和用戶需求,系統吞吐量和用戶感知的QoS。
In next-generation heterogeneous networks, the coexistence of 5G NR base stations and Wi-Fi access points in the unlicensed frequency bands presents a challenge. While both technologies aim to enhance transmission rates, the contention for these unlicensed bands is not always equitable. The 3GPP LAA standards introduce the LBT technology to facilitate the coexistence of NR base stations and Wi-Fi access points. However, the fairness of this contention is in question as throughput with LBT in LAA significantly outperforms CSMA/CA in Wi-Fi.
The performance of Wi-Fi in this coexistence scenario heavily depends on how LAA configures the LBT parameters. To address this issue, the paper proposes a novel radio resource allocation scheme. This scheme adjusts the TXOP duration of LAA using RL to improve fairness between LAA and Wi-Fi. Striking a balance between user fairness and system throughput is a challenging task, as these goals often conflict and are hard to improve simultaneously.
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