Rotatable RIS-Assisted Edge Computing: Orientation, Task Offloading, and Resource Optimization

TL;DR

This work addresses energy minimization in a rotatable RIS-assisted MEC system with multiple moving UEs by formulating a sequential decision problem. It develops a soft actor-critic (SAC) policy to jointly optimize RIS orientation $δ$, discrete phase shifts $φ_n$, task offloading $α_k[q]$, and resource allocation, while an inner optimization computes optimal local/edge computation and transmit power via a fractional programming approach. The approach achieves faster convergence and substantial energy savings (up to 47.3% versus fixed RIS) and demonstrates the benefits of mobility-aware, orientation-adaptive RIS in MEC. The results highlight the practical viability of orientation-aware RIS for enhancing MEC performance in dynamic wireless environments.

Abstract

The rotatable reconfigurable intelligent surface (RIS) can enhance mobile edge computing (MEC) performance by optimizing its orientation to improve the gain of received and transmitted signals. This correspondence investigates a rotatable RIS-assisted MEC system, aimed at minimizing energy consumption for multiple moving user equipment (UEs) through the joint design of RIS orientation, discrete phase shift, computation resource allocation, transmitting power and task offloading strategies. Considering the mobility of UEs, this problem is formulated as a sequential decision-making across multiple time slots. To address this challenge, a soft actor-critic (SAC)-based algorithm is proposed to optimize RIS orientation, phase shift and task offloading strategies, while computation resource allocation and transmitting power are determined based on the actions. Numerical results demonstrate that the proposed scheme exhibits superior convergence and performance compared to benchmarks. Additionally, the rotatable RIS scheme reduces total energy consumption by up to 47.3% compared to the fixed RIS, enhancing MEC system performance.

Figures (6)

• Figure 1: System model and the corresponding angle variations during RIS rotation.
• Figure 2: Training workflow of the SAC-based algorithm.
• Figure 3: Convergence performance comparison for SAC and PPO, where $K=5$.
• Figure 4: Performance VS the number of RIS elements, where $K=12$.
• Figure 5: Performance VS UEs number, where $N=20$ and $B=$ 3 MHz.