Reconfigurable Intelligent Surface-Aided Energy-Efficient Mobile Edge Computing in OFDM Systems

This letter proposes a novel resource allocation algorithm to enable a reconfigurable intelligent surface (RIS) to support delay-bounded mobile edge computing (MEC) in orthogonal frequency division multiplexing (OFDM) settings, adhering to per-subcarrier power constraint. The energy requirement of the smart devices (SDs) is minimized through the joint optimization of task offloading parameters, RIS configuration, and the receive beamforming of the base station (BS). A key contribution is that we find the semi-closed-form receive beamformers as a function of the RIS phases. Hence, the joint optimization is decoupled between convex offloading scheduling and non-convex RIS configuration by alternating optimization (AO). Another contribution is that we convexify the per-subcarrier power constraint by exploiting successive convex approximation (SCA), and acquiring a Karush-Kuhn-Tucker (KKT) point solution for the RIS configuration. Numerical results show the superiority of the new approach compared with baseline schemes in energy saving, and reveal that the optimal RIS location hinges on the channel quality differences among the RIS, BS, and SDs.