arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2107.09574eess.SPcs.AI
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通过通感一体化加速边缘智能

Accelerating Edge Intelligence via Integrated Sensing and Communication

Southern University of Science and Technology(南方科技大学);Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences(中国科学院深圳先进技术研究院);Shenzhen Research Institute of Big Data(深圳市大数据研究院)

Tong Zhang, Shuai Wang, Guoliang Li, Fan Liu, Guangxu Zhu, Rui Wang

摘要:

实现边缘智能包括感知、通信、训练和推理阶段。传统上,感测和通信阶段顺序执行,这导致数据集生成和上传时间过多。本文提出通过通感一体化(ISAC)来加速边缘智能。因此,传感和通信阶段被合并,以便最好地利用无线信号用于数据集生成和上传的双重目的。然而,ISAC还在感测和通信功能之间引入了额外的干扰。为了解决这个问题,本文提出了一种分类误差最小化公式来设计ISAC波束形成和时间分配。通过秩-1保证半定松弛的全局最优解,并进行性能分析,以量化ISAC增益超过传统的边缘智能。仿真结果验证了所提出的ISAC辅助边缘智能系统的有效性。有趣的是,我们发现当生成样本的持续时间大于上传样本的持续时间时,ISAC总是有益的。否则,ISAC增益可以为零甚至为负。然而,我们仍然得到了一个充分条件,在此条件下,正的ISAC增益是可行的。

英文摘要:

Realizing edge intelligence consists of sensing, communication, training, and inference stages. Conventionally, the sensing and communication stages are executed sequentially, which results in excessive amount of dataset generation and uploading time. This paper proposes to accelerate edge intelligence via integrated sensing and communication (ISAC). As such, the sensing and communication stages are merged so as to make the best use of the wireless signals for the dual purpose of dataset generation and uploading. However, ISAC also introduces additional interference between sensing and communication functionalities. To address this challenge, this paper proposes a classification error minimization formulation to design the ISAC beamforming and time allocation. The globally optimal solution is derived via the rank-1 guaranteed semidefinite relaxation, and performance analysis is performed to quantify the ISAC gain over that of conventional edge intelligence. Simulation results are provided to verify the effectiveness of the proposed ISAC-assisted edge intelligence system. Interestingly, we find that ISAC is always beneficial, when the duration of generating a sample is more than the duration of uploading a sample. Otherwise, the ISAC gain can vanish or even be negative. Nevertheless, we still derive a sufficient condition, under which a positive ISAC gain is feasible.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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