论文标题
可重新配置的智能表面辅助移动边缘计算通过异构学习任务
Reconfigurable Intelligent Surface Assisted Mobile Edge Computing with Heterogeneous Learning Tasks
论文作者
论文摘要
人工智能（AI）的不断增长和快速改善已经提高了无线网络的发展。移动边缘计算（MEC）为AI应用程序提供了一个自然平台，因为它具有丰富的计算资源来培训机器学习（ML）模型，以及对移动和Internet（IoT）设备生成的数据的低延迟访问。在本文中，我们提出了一个基础架构，可以在可重构智能表面（RIS）的帮助下在MEC服务器上执行ML任务。与主要标准以最大化吞吐量的常规通信系统相反，我们旨在最大化学习绩效。具体而言，我们通过共同优化移动用户的传输功率，基站（BS）（BS）和RIS的相移矩阵来最大程度地减少所有参与用户的最大学习错误。 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解决相移矩阵的具有挑战性的非凸优化问题。仿真结果表明，部署RIS并验证我们提出的算法比各种基准的优势。最后，基于Carla平台和第二个网络开发了一个统一的通信 - 培训推断平台，并在开发的平台上展示了该计划的用例（自动驾驶中的3D对象检测）。
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.
