Power Minimizing MEC Offloading with QoS Constraints over RIS-Empowered Communications

Abstract

This work lies at the intersection of two cutting edge technologies envisioned to proliferate in future 6G wireless systems: Multi-access Edge Computing (MEC) and Reconfigurable Intelligent Surfaces (RISs). While the former will bring a powerful information technology environment at the wireless edge, the latter will enhance communication performance, thanks to the possibility of adapting wireless propagation as per end users' convenience, according to specific service requirements. We propose a joint optimization of radio, computing, and wireless environment reconfiguration through an RIS, with the goal of enabling low power computation offloading services with reliability guarantees. Going beyond previous works on this topic, multi-carrier frequency selective RIS elements' responses and wireless channels are considered. This opens new challenges in RIS optimization, accounting for frequency dependent RIS response profiles, which strongly affect RIS-aided wireless links and, as a consequence, MEC service performance. We formulate an optimization problem accounting for short and long-term constraints involving device transmit power allocation across multiple subcarriers and local computing resources, as well as RIS reconfiguration parameters according to a recently developed Lorentzian model. Besides a theoretical optimization framework, numerical results show the effectiveness of the proposed method in enabling low power reliable computation offloading over RISaided frequency selective channels.

Jointly power allocation and phase shift optimization for RIS empowered downlink cellular networks

“…To evaluate the effectiveness of networks parameters to the proposed scheme, the authors considered the optimization problem with two object functions including the probabilities of task offloading and energy transfer efficiency. It is worth to mention that in RIS-aided MEC system, the radio, computing, and wireless environment are considered to optimize [26,27]. In line with this view, the objective in [26] is to optimize resource allocation including the transmit power and the computing capacity of the RIS-assisted MEC system.…”

“…It is worth to mention that in RIS-aided MEC system, the radio, computing, and wireless environment are considered to optimize [26,27]. In line with this view, the objective in [26] is to optimize resource allocation including the transmit power and the computing capacity of the RIS-assisted MEC system. It is highlighted that the authors formulated the RIS optimization problem for dependent RIS response profiles over the multicarrier frequency selective channels.…”