Computation Rate Maximization in UAV-Enabled Wireless-Powered Mobile-Edge Computing Systems

By Fuhui Zhou, Yongpeng Wu, Rose Qingyang Hu, Yi Qian. IEEE Journal on Selected Areas in Communications, 2018

700 citations recorded by the source corpus.

Mobile-edge computing (MEC) and wireless power transfer are two promising techniques to enhance the computation capability and to prolong the operational time of low-power wireless devices that are ubiquitous in Internet of Things. However, the computation performance and the harvested energy are significantly impacted by the severe propagation loss. In order to address this issue, an unmanned aerial vehicle (UAV)-enabled MEC wireless-powered system is studied in this paper. The computation rate maximization problems in a UAV-enabled MEC wireless powered system are investigated under both partial and binary computation offloading modes, subject to the energy-harvesting causal constraint and the UAV’s speed constraint. These problems are non-convex and challenging to solve. A two-stage algorithm and a three-stage alternative algorithm are, respectively, proposed for solving the formulated problems. The closed-form expressions for the optimal central processing unit frequencies, user offloading time, and user transmit power are derived. The optimal selection scheme on whether users choose to locally compute or offload computation tasks is proposed for the binary computation offloading mode. Simulation results show that our proposed resource allocation schemes outperform other benchmark schemes. The results also demonstrate that the proposed schemes converge fast and have low computational complexity.

Subfields: Aerospace Engineering, Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/d6c794669dc9bcbe15660e430d8bca6d4f907754

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Citations in the corpus

Multi-Agent Reinforcement Learning Based Resource Management in MEC- and UAV-Assisted Vehicular Networks (2021, background): “As the transmit powers of the MeNB and UAVs are much higher than that of a vehicle and the data size of each task’s process result is relatively small [29], the time consumption on downlinking the task process result to each vehicle is neglected here [12].”
UAV assistance paradigm: State-of-the-art in applications and challenges (2020, background): “This is why researchers have to explore more deeply these challenges and to enhance the functionality of existing approaches.”
Evolution of Non-Terrestrial Networks From 5G to 6G: A Survey (2021, background): “4) Resource Allocation: The authors in [176] analyzed a computation rate maximization problem for the MEC-enabled wireless powered networks under two schemes, viz.”
Accessing From the Sky: A Tutorial on UAV Communications for 5G and Beyond (2019, unknown): “[253] F.”
Energy Efficient Resource Allocation in UAV-Enabled Mobile Edge Computing Networks (2019, methodology): “Although the computation and communication power consumption of the UAV is considered in [41], the mechanical power of the UAV is ignored.”
Joint Task Offloading and Resource Allocation in UAV-Enabled Mobile Edge Computing (2020, background): “Hu et al. [22], Jeong et al. [33], and Zhou et al. [34] only considered communication and computation interactions between two types of entities where ground MUs ofﬂoad the tasks to UAV for computation.”
Joint Computation and Communication Design for UAV-Assisted Mobile Edge Computing in IoT (2019, background): “It is assumed that the wireless channel between the UAV and TD k is dominated by LoS link [19]”
UAV Trajectory Planning for Data Collection from Time-Constrained IoT Devices (2020, methodology): “Next, we employ an approximate to function log 2 (1 + Υ i,n ) by convex approximation with respect to ( 36], where at the r th iteration, the following inequality can be obtained, where .”
UAV-Assisted Relaying and Edge Computing: Scheduling and Trajectory Optimization (2018, methodology): “The solution of B U,k [n] and B down∗ U,k [n] in (33) and (34) can be obtained in a similar way [21].”
A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art (2019, unknown): “d to maximize the computation rate by jointly optimizing the transmit energy beamforming at the ET, as well as the communication and computation resource allocations at both the user and its helpers. [121] considered an WPT-based UAV-assisted MEC system in which an UAV acts as an MEC-enabled BS offe”