arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2012.13533cs.ITcs.AIcs.LGmath.IT
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可重构智能表面辅助异构学习任务的移动边缘计算

Reconfigurable Intelligent Surface Assisted Mobile Edge Computing with Heterogeneous Learning Tasks

Southern University of Science and Technology(南方科技大学);The University of Hong Kong(香港大学);Peng Cheng Laboratory(鹏城实验室);South China University of Technology(华南理工大学)

Shanfeng Huang, Shuai Wang, Rui Wang, Miaowen Wen, Kaibin Huang

摘要:

人工智能的日益普及和快速发展引发了人们对无线网络演进的重新思考。移动边缘计算(MEC)为人工智能应用提供了一个天然的平台,因为它具有丰富的计算资源来训练机器学习(ML)模型,以及对移动和物联网(IoT)设备生成的数据的低延迟访问。在本文中,我们提出了一种在MEC服务器上执行ML任务的基础设施,并借助可重新配置的智能表面(RIS)。在传统的通信系统的主要标准是最大限度地提高吞吐量相比,我们的目标是最大限度地提高学习性能。具体而言,我们最大限度地减少所有参与用户的最大学习误差,通过联合优化移动用户的发射功率,基站(BS)的波束成形向量,和RIS的相移矩阵。提出了一种基于交替优化(AO)的框架来迭代地优化这三项,其中开发了基于连续凸近似(SCA)的算法来求解功率分配问题,推导了波束形成向量的封闭形式表达式,设计了一种基于交替方向乘法器(ADMM)和误差水平搜索(ELS)的算法框架,以有效地解决具有挑战性的非凸优化问题的相移矩阵。仿真结果表明,部署RIS的显着收益,并验证了我们提出的算法在各种基准的优势。最后,基于CARLA平台和SECOND网络开发了一个统一的通信-训练-推理平台,并在该平台上演示了该方案的一个用例(自动驾驶中的三维目标检测)。

英文摘要:

The ever-growing popularity and rapid improving of artificial intelligence (AI) have raised rethinking on the evolution of wireless networks. Mobile edge computing (MEC) provides a natural platform for AI applications since it is with rich computation resources to train machine learning (ML) models, as well as low-latency access to the data generated by mobile and internet of things (IoT) devices. In this paper, we present an infrastructure to perform ML tasks at an MEC server with the assistance of a reconfigurable intelligent surface (RIS). In contrast to conventional communication systems where the principal criterions are to maximize the throughput, we aim at maximizing the learning performance. Specifically, we minimize the maximum learning error of all participating users by jointly optimizing transmit power of mobile users, beamforming vectors of the base station (BS), and the phase-shift matrix of the RIS. An alternating optimization (AO)-based framework is proposed to optimize the three terms iteratively, where a successive convex approximation (SCA)-based algorithm is developed to solve the power allocation problem, closed-form expressions of the beamforming vectors are derived, and an alternating direction method of multipliers (ADMM)-based algorithm is designed together with an error level searching (ELS) framework to effectively solve the challenging nonconvex optimization problem of the phase-shift matrix. Simulation results demonstrate significant gains of deploying an RIS and validate the advantages of our proposed algorithms over various benchmarks. Lastly, a unified communication-training-inference platform is developed based on the CARLA platform and the SECOND network, and a use case (3D object detection in autonomous driving) for the proposed scheme is demonstrated on the developed platform.

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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