ShanghaiTech University Knowledge Management System
Reconfigurable Intelligent Surface for Green Edge Inference in Machine Learning | |
2019-12 | |
会议录名称 | 2019 IEEE GLOBECOM WORKSHOPS (GC WKSHPS)
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页码 | 1-6 |
发表状态 | 已发表 |
DOI | 10.1109/GCWkshps45667.2019.9024398 |
摘要 | To provide energy-efficient machine learning services for high-stake applications e.g., drones and autonomous cars, in this paper, we propose a reconfigurable intelligent surface (RIS)-empowered edge inference architecture by cooperatively executing tasks at multiple computing- enabled base stations. Specifically, an RIS with many reflecting elements is deployed in the network architecture to assist the controllable signal propagations via configuring the phase shifts of these elements, thereby enhancing the signal quality at receivers. To minimize the power consumption for RIS-empowered edge inference process, we shall propose a joint group sparse beamforming and phase shifts design approach for inference tasks allocation and signal propagations control, respectively. An alternating minimization method is further developed to decouple the optimization variables and split this intractable problem into two subproblems. The mixed $\ell_{1,2}$-norm and difference-of-convex-functions (DC) techniques are presented respectively for group sparsity inducing and phase shifts design. Simulation results demonstrate the admirable performance gains of the proposed algorithms and the effectiveness of the deployment of RIS. |
关键词 | Task analysis Array signal processing Power demand Resource management Downlink Machine learning Base stations |
会议地点 | Waikoloa, HI, USA |
会议日期 | 9-13 Dec. 2019 |
URL | 查看原文 |
收录类别 | EI ; CPCI ; CPCI-S |
语种 | 英语 |
原始文献类型 | Conferences |
来源库 | IEEE |
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/114790 |
专题 | 信息科学与技术学院_硕士生 信息科学与技术学院_PI研究组_石远明组 |
作者单位 | ShanghaiTech University |
第一作者单位 | 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 | Sheng Hua,Yuanming Shi. Reconfigurable Intelligent Surface for Green Edge Inference in Machine Learning[C],2019:1-6. |
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