Joint Optimization of USVs Communication and Computation Resource in IRS-Aided Wireless Inland Ship MEC Networks
Qingsong Ai, Xinhui Qiao, Yangzhe Liao, Quan Yu
Abstract
To realize fully functioning inland waterway communications, portable and affordable unmanned surface vehicles (USVs) have become increasingly significant in wireless inland ship networks. However, USVs are struggling in handling resource-intensive services due to the limited battery and CPU resources. In addition, the power of signal transmitted between USVs and the shore base stations suffers from considerable attenuation. In this paper, an intelligent reflecting surface (IRS)-aided wireless inland ship mobile edge computing (MEC) network architecture is proposed to support USVs’ communication and computation. The minimization of network energy consumption is formulated under a number of QoS requirements. To achieve an efficient solution to the formulated challenging problem, a low-complexity enhanced block coordinate descent algorithm is proposed and thus the original optimization problem can be divided into a list of sub-problems. The extensive results verify the effectiveness of jointly considering USVs’ offloading decisions, USVs’ transmission power, unmanned aerial vehicle (UAV) trajectory and IRS phase shift-vector design for energy efficiency in a spare wireless inland ship MEC network and show that the proposed algorithm can decrease energy consumption by approximately 75% compared to a baseline algorithm without optimization design. Also, the network performance under different IRS elements and accuracy parameters are investigated.