Energy Efficiency Aware Computation Offloading in RIS-Enhanced Symbiotic Radio Systems

Abstract

This paper studies symbiotic radio (SR)-enabled computation offloading with the aid of the reconfigurable intelligent surface (RIS) for ultra-low-power or even battery-free Internet-of-Things (IoT). Specifically, multiple IoT devices can passively modulate their small-size computation tasks over computation offloading transmission of the primary transmitter (PTx) by backscattering. The multi-antenna base station integrated with the mobile edge computing (MEC) server conducts joint decoding to separate and recover data of computation tasks from the PTx and IoT devices. Our target is to maximize the computation energy efficiency (CEE) of the PTx while guaranteeing IoT devices' computation requirements under PTx's resource constraints. The passive beamforming of the RIS, the receive beamforming at the receiver, as well as the local computing frequency and transmission power of the PTx, are jointly optimized. We utilize the Dinkelbach algorithm to transform the problem into a tractable form and then use the AO technique to decouple it into several subproblems, which are solved by convex optimization methods. Simulation results show that with our algorithm, not only the task offloading of IoT devices can be supported effectively and incidentally in the RIS-enhanced SR system, but also the CEE of the PTx is improved significantly.