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The ever-growing popularity and rapid improving of artificial intelligence(AI) have raised rethinking on the evolution of wireless networks. Mobile edgecomputing (MEC) provides a natural platform for AI applications since it iswith rich computation resources to train machine learning (ML) models, as wellas 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 tasksat an MEC server with the assistance of a reconfigurable intelligent surface(RIS). In contrast to conventional communication systems where the principalcriterions are to maximize the throughput, we aim at maximizing the learningperformance. Specifically, we minimize the maximum learning error of allparticipating users by jointly optimizing transmit power of mobile users,beamforming vectors of the base station (BS), and the phase-shift matrix of theRIS. An alternating optimization (AO)-based framework is proposed to optimizethe 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 analternating direction method of multipliers (ADMM)-based algorithm is designedtogether with an error level searching (ELS) framework to effectively solve thechallenging nonconvex optimization problem of the phase-shift matrix.Simulation results demonstrate significant gains of deploying an RIS andvalidate the advantages of our proposed algorithms over various benchmarks.Lastly, a unified communication-training-inference platform is developed basedon the CARLA platform and the SECOND network, and a use case (3D objectdetection in autonomous driving) for the proposed scheme is demonstrated on thedeveloped platform.

Reconfigurable Intelligent Surface Assisted Mobile Edge Computing with Heterogeneous Learning Tasks