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Adaptive Multi-objective Reinforcement Learning for Pareto Frontier Approximation: A Case Study of Resource Allocation Network in Massive MIMO | |
2021 | |
会议录名称 | 29TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO 2021)
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ISSN | 2076-1465 |
卷号 | 2021-August |
页码 | 1631-1635 |
发表状态 | 已发表 |
DOI | 10.23919/EUSIPCO54536.2021.9615934 |
摘要 | Multi-Objective Optimization (MOO) has always been an important issue in the field of wireless communications. With the development of 5G networks, more objectives have been concerned to improve the user experience. The relationship between these multiple objectives is complex or even conflicting, which increases the difficulty of solving the MOO problems. Traditional multi-objective optimization algorithms (e.g., genetic algorithm) have higher computation complexity and require to store multiple models for the preference of different objectives. Therefore, in this paper, a multi-objective scheduling model based on the Actor-Critic framework is proposed, which can effectively solve the multi-user scheduling problem under Massive Multiple-Input Multiple-Output (MIMO), and utilize a single model to approximate the Pareto frontier. In the single-cell downlink scheduling scenario, the proposed model is applied to the two objective optimization, i.e., channel capacity and fairness. The simulation results show that the performance of our model is close to the theoretical optimal value in the single-objective case. The Pareto frontier can be uniformly approximated in the multi-objective case, and it has strong robustness to never-seen preference combinations. |
关键词 | Massive MIMO multi-objective reinforcement learning (MORL) Pareto frontier single cell Multi-User (MU)-MIMO scheduling |
会议名称 | 29th European Signal Processing Conference (EUSIPCO) |
出版地 | PO BOX 74251, KESSARIANI, 151 10, GREECE |
会议地点 | ELECTR NETWORK |
会议日期 | AUG 23-27, 2021 |
URL | 查看原文 |
收录类别 | EI ; CPCI ; CPCI-S |
语种 | 英语 |
WOS研究方向 | Acoustics ; Computer Science ; Engineering ; Imaging Science & Photographic Technology ; Telecommunications |
WOS类目 | Acoustics ; Computer Science, Software Engineering ; Engineering, Electrical & Electronic ; Imaging Science & Photographic Technology ; Telecommunications |
WOS记录号 | WOS:000764066600325 |
出版者 | EUROPEAN ASSOC SIGNAL SPEECH & IMAGE PROCESSING-EURASIP |
EI入藏号 | 20220411505662 |
引用统计 | 正在获取...
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文献类型 | 会议论文 |
条目标识符 | https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/176075 |
专题 | 信息科学与技术学院_博士生 信息科学与技术学院_PI研究组_杨旸组 信息科学与技术学院_PI研究组_周勇组 信息科学与技术学院_硕士生 创意与艺术学院_特聘教授组_汪军组 |
通讯作者 | Chen, Ruiqing |
作者单位 | 1.ShanghaiTech Univ, Shanghai, Peoples R China 2.UCL, London, England 3.Shanghai Inst Microsyst & Informat Technol, Shanghai, Peoples R China 4.Univ Chinese Acad Sci, Beijing, Peoples R China |
第一作者单位 | 上海科技大学 |
通讯作者单位 | 上海科技大学 |
第一作者的第一单位 | 上海科技大学 |
推荐引用方式 GB/T 7714 | Chen, Ruiqing,Sun, Fanglei,Chen, Liang,et al. Adaptive Multi-objective Reinforcement Learning for Pareto Frontier Approximation: A Case Study of Resource Allocation Network in Massive MIMO[C]. PO BOX 74251, KESSARIANI, 151 10, GREECE:EUROPEAN ASSOC SIGNAL SPEECH & IMAGE PROCESSING-EURASIP,2021:1631-1635. |
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