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). To significantly reduce the delay during data offloading and downloading, this architecture relies on the benefits of line-of-sight (LoS) 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 (3-D) geometrical representation of the MEC-enabled network is introduced and an optimization method is proposed that minimizes the computation-based and communication-based weighted total energy consumption (WTEC) of vehicles and ARSU subject to transmit power allocation, task allocation, and time slot scheduling. The results verify the theoretical derivations, emphasize on the effectiveness of the LoS massive MIMO transmission, and provide useful engineering insights.
Highlights
With the emergence of the big data era at vehicular networks, the Internet of Vehicles (IoV) paradigm, and the vehicularto-everything (V2X) information interaction, a vast number of connected automobile terminals equipped with computation and multi-communication units will pave the path for novel services [1]
A unmanned aerial vehicle (UAV)-aided mobile edge computing (MEC)-enabled IoV architecture that relies on reconfigurable intelligent surface (RIS) units was proposed in [32]
CONTRIBUTION Motivated by the aforementioned observations, we investigate a massive MIMO UAV-aided MEC-enabled vehicular network
Summary
With the emergence of the big data era at vehicular networks, the Internet of Vehicles (IoV) paradigm, and the vehicularto-everything (V2X) information interaction, a vast number of connected automobile terminals equipped with computation and multi-communication units will pave the path for novel services [1]. In order to minimize the total network delay, an edge intelligence empowered IoV framework was constructed in [11] and an online algorithm relying on Lyapunov optimization was designed to manage computation offloading and content caching Despite such promising computing capabilities, attaining ubiquitous connectivity and sufficient radio coverage between vehicles and MEC servers is challenging, since ground RSUs (GRSUs) often struggle in areas with obstacles and highly mobile and disperse nodes. A UAV-aided MEC-enabled IoV architecture that relies on reconfigurable intelligent surface (RIS) units was proposed in [32] On another front, massive multiple-input–multiple-output (MIMO) technology has recently received unprecedented attention as a key enabler for increased spectral and energy efficiency, drastically reduced round-trip latency, and support of highly-intensive computation tasks for a vast number of connected users [33].
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