Enhance Latency-Constrained Computation in MEC Networks Using Uplink NOMA

Abstract

Non-orthogonal multiple access (NOMA) based mobile edge computing (MEC) networks can enhance delay-constrained computation. Most of the existing works focus on the energy or delay minimization, and the study on NOMA based MEC in terms of successful computation probability has been limited, which motivates this work. In particular, randomly deployed edge computing users are modeled as the homogeneous Poisson Point Process and are divided into two groups namely center group (C-group) and edge group (E-group) for uplink NOMA grouping. We propose a new hybrid offloading scheme in a NOMA-based MEC network that can operate in three different modes, namely partial offloading, complete local computation, and complete offloading. We firstly consider a NOMA grouping scenario where a user from the C-group and a user from the E-group are each given a fixed location. The probability that the computation can be completed within the given delay budget is derived and the optimal parameters, i.e., the time for offloading, the power allocation, and the offloading ratios for the two fixed users, are obtained. It reveals that the optimal offloading ratios are determined by the difference of the computational capability between the edge computing user and the MEC server, and that the locations of users have a big impact on the successful computation probability. Inspired by this, we further study three distance-dependent NOMA grouping schemes in the MEC offloading. Specifically, we provide closed-form mathematical expressions of the successful computation probability and its partial optimal solutions for these three schemes. Simulation results verify the accuracy of the analytical results and compare the performance of the proposed offloading scheme with the existing schemes. Insights on the pros and the cons of different user selection schemes are also provided.