Reconfigurable Intelligent Surface Assisted Mobile Edge Computing With Heterogeneous Learning Tasks

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.

Automated summary

This article proposes an infrastructure for performing machine learning tasks using mobile edge computing and reconfigurable intelligent surfaces. By optimizing the transmit power of mobile users, beamforming vectors of the base station, and the phase-shift matrix of the intelligent surface, the learning performance is maximized. The developed algorithms and platform demonstrate a use case of 3D object detection in autonomous driving.