Power Minimization in Active RIS-Assisted NOMA-MEC Networks

Abstract

In this letter, we investigate a mobile edge computing (MEC) system enabled by an active reconfigurable intelligent surface (ARIS)-assisted uplink non-orthogonal multiple access (NOMA) transmission. We propose an ARIS-assisted uplink NOMA-MEC scheme, termed as ARIS-NOMA, in which multiple users perform local computation while concurrently offloading computational tasks to a base station (BS). We formulate a total power consumption minimization problem by jointly optimizing the BS’s receive beamforming (RBF), ARIS reflection matrix, local computation frequency (LCF), and transmit power of each user. To address this problem, we propose a block coordinate descent (BCD) method to decouple the formulated problem into a series of sub-problems that are solved iteratively by optimizing one variable block while fixing the others. Specifically, a closed-form optimal solution is derived for the RBF, the ARIS reflection matrix is efficiently obtained via a sequential rank-one constraint relaxation (SROCR) algorithm, and the optimal LCF and transmit power are determined using the Lagrange dual method. Numerical results demonstrate that the proposed ARIS–NOMA scheme significantly reduces the total system power consumption compared with conventional benchmark schemes.