  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.
