  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
provides rich computation resources to train AI models, as well as low-latency
access to the data generated by mobile and Internet of Things devices. In this
paper, we present an infrastructure to perform machine learning tasks at an MEC
server with the assistance of a reconfigurable intelligent surface (RIS). In
contrast to conventional communication systems where the principal criteria are
to maximize the throughput, we aim at optimizing the learning performance.
Specifically, we minimize the maximum learning error of all users by jointly
optimizing the beamforming vectors of the base station and the phase-shift
matrix of the RIS. An alternating optimization-based framework is proposed to
optimize the two terms iteratively, where 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 framework to effectively solve the 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.
