Improving Computing Capability for Active RIS-Assisted NOMA-MEC Networks
Yizhi Li, Yulong Zou, Hui Hao, Jia Zhu, Boyu Ning
Abstract
In this paper, we consider an active reconfigurable intelligent surface (ARIS)-assisted uplink mobile edge computing (MEC) system. We propose an ARIS assisted non-orthogonal multiple access (NOMA) scheme, denoted by ARIS-NOMA, in which multiple users process their local computing and simultaneously perform task offloading in NOMA manner to a base station (BS) with the help of a common ARIS. A total computation bits (TCB) maximization problem is formulated to improve the computing capability of proposed ARIS-NOMA scheme by jointly optimizing the BS’s receive beamforming (RBF), ARIS’s reflection coefficients, local computing frequency (LCF), and transmit power of each user. Specifically, we first use a fractional programming (FP) technique to reformulate the original objective function, and then utilize a block coordinate descent (BCD) method to separate the reformulated problem into several sub-problems and iteratively optimize one set of variables with others being fixed. In particular, we obtain closed-form solutions for BS RBF, ARIS reflection coefficients, LCF, and the transmit power of each user by jointly leveraging the Lagrange dual method and Karush-Kuhn-Tucker (KKT) conditions. Numerical results validate the advantages of the proposed ARIS-NOMA scheme compared to the conventional benchmarks regarding to the TCB.