The ever-growing popularity and rapid development 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 has 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 article, 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 are derived to solve the beamforming design problem, and an alternating direction method of multipliers (ADMM)-based algorithm is designed to efficiently solve the phase-shift matrix design problem. Simulation results demonstrate significant gains of deploying an RIS and validate the advantages of our proposed algorithms over various benchmarks. Lastly, a unified sensing-communication-learning platform is developed based on the CARLA simulator and the SECOND network, and a use case (3D object detection in autonomous driving) for the proposed scheme is demonstrated on the developed platform.

中文翻译：

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具有异构学习任务的可重构智能表面辅助移动边缘计算

人工智能（AI）的日益普及和快速发展引发了对无线网络演进的重新思考。移动边缘计算 (MEC) 为 AI 应用程序提供了一个自然平台，因为它拥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，我们展示了一种在可重构智能表面 (RIS) 的帮助下在 MEC 服务器上执行 ML 任务的基础设施。与主要标准是最大化吞吐量的传统通信系统相比，我们的目标是最大化学习性能。具体来说，我们通过联合优化移动用户的发射功率来最小化所有参与用户的最大学习误差，基站 (BS) 的波束成形矢量和 RIS 的相移矩阵。提出了一种基于交替优化 (AO) 的框架来迭代优化三项，其中开发了基于逐次凸逼近 (SCA) 的算法来解决功率分配问题，并推导出封闭形式的表达式来解决波束成形设计问题, 并设计了一种基于交替方向乘法器 (ADMM) 的算法来有效解决相移矩阵设计问题。仿真结果证明了部署 RIS 的显着收益，并验证了我们提出的算法相对于各种基准的优势。最后，基于CARLA模拟器和SECOND网络开发了统一的传感-通信-学习平台，