Joint Mobility, Communication and Computation Optimization for UAVs in Air-Ground Cooperative Networks
TL;DR: In this paper, a UAV-oriented computation offloading system is investigated, where the UAV desires to complete its onboard computation demands with the assistance of a ground edge-computing infrastructure, i.e., a road-side unit (RSU).
read more
Abstract: Unmanned aerial vehicles (UAVs) play a significant role in various 5G or Beyond-5G (B5G)-enabled Internet-of-Things (IoT) applications. However, the UAV performance in an air-ground cooperative network is significantly affected by its mobility and air-to-ground (A2G) communication and computation behaviors. In this paper, a UAV-oriented computation offloading system is investigated, where the UAV desires to complete its onboard computation demands with the assistance of a ground edge-computing infrastructure, i.e., a road-side unit (RSU). The objective is to maximize the energy efficiency of the UAV. Specifically, a non-convex constrained optimal control problem is formulated to optimize the overall energy efficiency of UAV by jointly considering the coupled effects of UAV's longitudinal mobility, A2G communication, and computation dynamics. To address the coupled complexity and non-convexity of the original problem, a primal decomposition approach is developed to transform the problem into a convex subproblem and a primary problem, and then a closed-form optimal transmission power control is derived by solving the subproblem, which is dependent on mobility information. By embedding the closed-form optimal power control into the primary problem, a gradient projection-based iterative algorithm is proposed to obtain a joint optimal solution for both the longitudinal acceleration control and the power control, the feasibility and convergence of which is also theoretically proven. Extensive simulations have been conducted to validate the effectiveness of the proposed method in terms of constraint satisfaction and convergence speed, and comparative results also demonstrate that it can outperform other benchmark methods in terms of global energy efficiency.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Figures
Fig. 12. Global performance comparison under different terminal velocities. 
TABLE I LIST OF SYMBOLS 
Fig. 11. Global performance comparison under different initial velocities. 
Fig. 10. Global performance comparison under different flying durations. 
Fig. 9. Global performance comparison under different computation demands. 
Fig. 8. The variation of the computation energy consumption.
Citations
Evolution of Non-Terrestrial Networks From 5G to 6G: A Survey
TL;DR: In this article , the authors comprehensively survey the evolution of non-terrestrial networks (NTNs) highlighting their relevance to 5G networks and essentially, how it will play a pivotal role in the development of 6G ecosystem.
252
Deep Reinforcement Learning Based Computation Offloading and Trajectory Planning for Multi-UAV Cooperative Target Search
TL;DR: In this paper , a deep reinforcement learning (DRL) technique was proposed to jointly make optimal computation offloading decisions and flying orientation choices for multi-UAV cooperative target search, and extensive simulations validate the effectiveness of the proposed techniques, and comprehensive discussions on how different parameters affect the search performance are given.
51
•Posted Content
Evolution of Non-Terrestrial Networks From 5G to 6G: A Survey.
Mohammad Mahdi Azari,Sourabh Solanki,Symeon Chatzinotas,Oltjon Kodheli,Hazem Sallouha,Achiel Colpaert,Jesus Fabian Mendoza Montoya,Sofie Pollin,Alireza Haqiqatnejad,Arsham Mostaani,Eva Lagunas,Bjorn Ottersten +11 more
TL;DR: In this article, a comprehensive survey of the evolution of non-terrestrial networks (NTNs) highlighting its relevance to 5G networks and how it will play a pivotal role in the development of 6G and beyond wireless networks is presented.
26
Deep Reinforcement Learning Based Computation Offloading and Trajectory Planning for Multi-UAV Cooperative Target Search
TL;DR: In this paper , a deep reinforcement learning (DRL) technique was proposed to jointly make optimal computation offloading decisions and flying orientation choices for multi-UAV cooperative target search, and extensive simulations validate the effectiveness of the proposed techniques, and comprehensive discussions on how different parameters affect the search performance are given.
26
MEC-Assisted Real-Time Data Acquisition and Processing for UAV With General Missions
TL;DR: In this paper , the authors studied the real-time data acquisition in a general mission scenario where a UAV acquires data in real time, which needs to be timely processed with the assistance of a mobile edge computing server.
17
References
Edge Computing: Vision and Challenges
TL;DR: The definition of edge computing is introduced, followed by several case studies, ranging from cloud offloading to smart home and city, as well as collaborative edge to materialize the concept of edge Computing.
7.1K
A Survey on Mobile Edge Computing: The Communication Perspective
TL;DR: A comprehensive survey of the state-of-the-art MEC research with a focus on joint radio-and-computational resource management is provided in this paper, where a set of issues, challenges, and future research directions for MEC are discussed.
4.6K
Wireless communications with unmanned aerial vehicles: opportunities and challenges
TL;DR: An overview of UAV-aided wireless communications is provided, by introducing the basic networking architecture and main channel characteristics, highlighting the key design considerations as well as the new opportunities to be exploited.
4.1K
•Posted Content
A Survey on Mobile Edge Computing: The Communication Perspective
TL;DR: A comprehensive survey of the state-of-the-art MEC research with a focus on joint radio-and-computational resource management and recent standardization efforts on MEC are introduced.
3.1K
Optimal LAP Altitude for Maximum Coverage
TL;DR: An analytical approach to optimizing the altitude of LAPs to provide maximum radio coverage on the ground shows that the optimal altitude is a function of the maximum allowed pathloss and of the statistical parameters of the urban environment, as defined by the International Telecommunication Union.