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

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 to road side (RSUs) collocated MEC servers. In this direction, hovering UAV can serve as an RSU (ARSU) task processing act relay and further offload ground (GRSU). To significantly reduce delay during data downloading, relies on benefits line-of-sight (LoS) massive multiple-input–multiple-output (MIMO). Therefore, it vehicles, ARSU, GRSU employ large-scale antennas. A three-dimensional (3-D) geometrical representation MEC-enabled network introduced optimization method proposed minimizes computation-based communication-based weighted total energy consumption (WTEC) ARSU subject transmit power allocation, time slot scheduling. The results verify theoretical derivations, emphasize effectiveness LoS MIMO transmission, provide useful engineering insights.