Joint mmWave Beamforming and Resource Allocation in NOMA-MEC Network for Internet of Things

Abstract

Mobile edge computing (MEC) technology can reduce the computational burden of Internet of Things (IoT) devices by offloading computationally intensive tasks to physically proximate edge servers. The combination of millimeter-wave (mmWave) massive multiple input multiple output (MIMO) and non-orthogonal multiple access (NOMA) technique has draw significant attention for its high spectral efficiency. This paper merges the computation offloading and mmWave massive MIMO-NOMA communication to seek energy efficient performance for IoT devices in MEC network. First, we propose an IoT device clustering algorithm to schedule all IoT devices into limited beams. Following this, we minimize the total energy consumption of computing and offloading in the NOMA-MEC network by jointly optimizing hybrid beamforming, offloading task assignment (OTA) ratio and power allocation. Then, the non-convex joint optimization problem is solved by proposing a suboptimal solution with two stages. In the first stage, a cyclic block-coordinate update algorithm is proposed for analog beamforming design, and then the digital beamforming vectors are obtained by zero-forcing method. In the second stage, an alternating minimization algorithm is proposed to optimize the OTA ratio and power allocation. Finally, simulation results are provided to validate the convergence of the proposed algorithms and the superiority of our proposed schemes over other benchmark schemes, which demonstrate the outstanding performance leveraging the massive MIMO-NOMA technique during computational offloading.