Reconfigurable Intelligent Surface for Green Edge Inference in Machine Learning
2019-12
会议录名称2019 IEEE GLOBECOM WORKSHOPS (GC WKSHPS)
页码1-6
发表状态已发表
DOI10.1109/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
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收录类别EI ; CPCI ; CPCI-S
语种英语
原始文献类型Conferences
来源库IEEE
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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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