RIS-Assisted Multi-Device Edge AI Inference

In this paper, we propose a multi-device co-inference system based on a task-oriented over-the-air computation (Air-Comp) via reconfigurable intelligent surface (RIS). Specially, local feature vectors extracted from the real-time noisy sensory data on devices are aggregated over-the-air by exploiting the waveform superposition in a multi-user channel. Then the aggregated features received at the server are fed into an inference model for decision making or control of actuators. Based on the proposed multi-device co-inference system, we jointly optimize the receive signal strength of the device, the beamforming vector, and RIS phase shifts to suppress the sensing and channel noise and maximize the inference accuracy. To solve the problem, we first transform the original problem into a convex difference (d.c.) problem, and convert the d.c. problem from the complex domain to the real domain. Then, we propose a successive convex approximation based approach to solve the problem in the real domain. With the supportive data and results from the application of human motion recognition, we show the proposed scheme achieves a higher inference accuracy then the conventional approaches.