Energy Optimization in Massive MIMO UAV-Aided MEC-Enabled Vehicular Networks

TL;DR

This paper proposes an energy-efficient UAV-assisted MEC architecture for vehicular networks using massive MIMO to reduce delay and optimize energy consumption during task offloading and processing.

Contribution

It introduces a 3D MEC network model with a novel optimization method for energy minimization leveraging massive MIMO in UAV-assisted vehicular networks.

Findings

Massive MIMO significantly reduces data offloading delay.

The proposed optimization minimizes total energy consumption effectively.

Numerical results validate the theoretical model and show performance gains.

Abstract

This paper presents a novel unmanned aerial vehicle (UAV) aided mobile edge computing (MEC) architecture for vehicular networks. It is considered that the vehicles should complete latency critical computation intensive tasks either locally with on-board computation units or by offloading part of their tasks to road side units (RSUs) with collocated MEC servers. In this direction, a hovering UAV can serve as an aerial RSU (ARSU) for task processing or act as an aerial relay and further offload the computation tasks to a ground RSU (GRSU). In order to significantly reduce the delay during data offloading and downloading, this architecture relies on the benefits of massive multiple input multiple output (MIMO). Therefore, it is considered that the vehicles, the ARSU, and the GRSU employ large scale antennas. A three dimensional (3D) geometrical representation of the MEC enabled network is introduced and an optimization method is proposed that minimizes the weighted total energy consumption (WTEC) of the vehicles and ARSU subject to transmit power allocation, task allocation, and timeslot scheduling. The numerical results verify the theoretical derivations, emphasize on the effectiveness of the massive MIMO transmission, and provide useful engineering insights.