Joint Placement and Resource Allocation for UAV-Assisted Mobile Edge Computing Networks with URLLC

doi:10.12142/ZTECOM.202002007

ZTE Communications ›› 2020, Vol. 18 ›› Issue (2): 49-56.DOI: 10.12142/ZTECOM.202002007

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Joint Placement and Resource Allocation for UAV-Assisted Mobile Edge Computing Networks with URLLC

ZHANG Pengyu¹, XIE Lifeng¹, XU Jie²()

^1.School of Information Engineering, Guangdong University of Technology, Guangzhou, Guangdong 510006, China
^2.Future Network of Intelligence Institute and School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen，Guangdong 518172, China

Received:2019-04-27 Online:2020-06-25 Published:2020-08-07
About author:ZHANG Pengyu received the B.E. degree from Guangdong University of Technology， China in 2017. He is pursuing his master degree in the School of Information Engineering, Guangdong University of Technology. His research interests include UAV communications, mobile edge computing, and ultra-reliable and low-latency communications.|XIE Lifeng received the B.E. degree from Guangdong University of Technology,China in 2016. He is currently a Ph.D. candidate in the School of Information Engineering, Guangdong University of Technology. His research interests include energy harvesting in wireless communications, wireless information and power transfer, and UAV communications.|XU Jie (xujie@cuhk.edu.cn) received the B.E. and Ph.D. degrees from University of Science and Technology of China in 2007 and 2012 respectively. From 2012 to 2014, he was a Research Fellow with the Department of Electrical and Computer Engineering, National University of Singapore. From 2015 to 2016, he was a post-doctoral Research Fellow with the Engineering Systems and Design Pillar, Singapore University of Technology and Design. From 2016 to 2019, he was a professor with the School of Information Engineering, Guangdong University of Technology, China. He is currently an associate professor with the School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, China. His research interests include energy efficiency and energy harvesting in wireless communications, wireless information and power transfer, UAV communications, and mobile edge computing and learning.
Supported by:
the Key Area R&D Program of Guangdong Province with(2018B030338001);the National Key R&D Program of China with(2018YFB1800800);Natural Science Foundation of China with(61871137);the Guangdong Province Basic Research Program (Natural Science) with(2018KZDXM028);Guangdong Zhujiang Project(2017ZT07X152);Shenzhen Key Lab Fund(ZDSYS201707251409055)

Abstract

Abstract:

This paper investigates an unmanned aerial vehicle (UAV) assisted mobile edge computing (MEC) network with ultra-reliable and low-latency communications (URLLC), in which a UAV acts as an aerial edge server to collect information from a set of sensors and send the processed data (e.g., command signals) to the corresponding actuators. In particular, we focus on the round-trip URLLC from the sensors to the UAV and to the actuators in the network. By considering the finite block-length codes, our objective is to minimize the maximum end-to-end packet error rate (PER) of these sensor-actuator pairs, by jointly optimizing the UAV’s placement location and transmitting power allocation, as well as the users’ block-length allocation, subject to the UAV’s sum transmitting power constraint and the total block-length constraint. Although the maximum-PER minimization problem is non-convex and difficult to be optimally solved, we obtain a high-quality solution to this problem by using the technique of alternating optimization. Numerical results show that our proposed design achieves significant performance gains over other benchmark schemes without the joint optimization.

Key words: UAV, MEC, URLLC, placement optimization, resource allocation

ZHANG Pengyu, XIE Lifeng, XU Jie. Joint Placement and Resource Allocation for UAV-Assisted Mobile Edge Computing Networks with URLLC[J]. ZTE Communications, 2020, 18(2): 49-56.

Figures/Tables 3

References 29

1	MAO Y Y, YOU C S, ZHANG J, et al. A survey on mobile edge computing: the communication perspective [J]. IEEE communications surveys & tutorials, 2017, 19(4): 2322-2358. DOI:10.1109/comst.2017.2745201 DOI
2	DING Z G, XU J, DOBRE O A, et al. Joint power and time allocation for NOMA-MEC offloading [J]. IEEE transactions on vehicular technology, 2019, 68(6): 6207-6211. DOI:10.1109/tvt.2019.2907253 DOI
3	SUN H J, ZHOU F H, HU R Q. Joint offloading and computation energy efficiency maximization in a mobile edge computing system [J]. IEEE transactions on vehicular technology, 2019, 68(9): 3052-3056. DOI:10.1109/tvt.2019.2893094 DOI
4	BAI T, WANG J J, REN Y, et al. Energy⁃efficient computation offloading for secure UAV⁃edge⁃computing systems [J]. IEEE transactions on vehicular technology, 2019, 68(6): 6074-6087. DOI:10.1109/tvt.2019.2912227 DOI
5	WANG F, XU J, DING Z G. Multi⁃antenna NOMA for computation offloading in multiuser mobile edge computing systems [J]. IEEE transactions on communications, 2019, 67(3): 2450-2463. DOI:10.1109/tcomm.2018.2881725 DOI
6	LIU J, MAO Y Y, ZHANG J, et al. Delay⁃optimal computation task scheduling for mobile⁃edge computing systems[C]//2016 IEEE International Symposium on Information Theory. Barcelona, Spain, 2016. DOI:10.1109/isit.2016.7541539 DOI
7	WANG F, XU J, WANG X, et al. Joint offloading and computing optimization in wireless powered mobile⁃edge computing systems [J]. IEEE transactions on wireless communications, 2018, 17 (3): 1784-1797. DOI:10.1109/twc.2017.2785305 DOI
8	ZHU G X, LIU D Z, DU Y Q, et al. Toward an intelligent edge: wireless communication meets machine learning [J]. IEEE communications magazine, 2020, 58(1): 19-25. DOI:10.1109/mcom.001.1900103 DOI
9	MO X P, XU J. Energy⁃efficient federated edge learning with joint communication and computation design [EB/OL]. (2020-2-29)[2020-4-1].
10	CUI Q M, GONG Z Z, NI W, et al. Stochastic online learning for mobile edge computing: learning from changes [J]. IEEE communications magazine, 2019, 57(3): 63-69. DOI:10.1109/mcom.2019.1800644 DOI
11	MOTLAGH N H, BAGAA M, TALEB T. UAV⁃Based IoT Platform: a crowd surveillance use case [J]. IEEE Communications Magazine, 2017, 55(2): 128-134. DOI:10.1109/mcom.2017.1600587cm DOI
12	ZENG Y, XU J, ZHANG R. Energy minimization for wireless communication with rotary⁃wing UAV [J]. IEEE transactions on wireless communications, 2019, 18(4): 2329-2345. DOI:10.1109/twc.2019.2902559 DOI
13	CAO X W, WANG F, XU J, et al. Joint computation and communication cooperation for energy⁃efficient mobile edge computing [J]. IEEE Internet of Things journal, 2019, 6(3): 4188-4200. DOI:10.1109/jiot.2018.2875246 DOI
14	ZENG Y, ZHANG R, LIM T J. Wireless communications with unmanned aerial vehicles: opportunities and challenges [J]. IEEE communications magazine, 2016, 54(5): 36-42. DOI:10.1109/mcom.2016.7470933 DOI
15	ZENG Y, ZHANG R, LIM T J. Throughput maximization for UAV⁃enabled mobile relaying systems [J]. IEEE transactions on communications, 2016, 64(12): 4983-4996. DOI:10.1109/tcomm.2016.2611512 DOI
16	XU J, ZENG Y, ZHANG R. UAV⁃enabled wireless power transfer: trajectory design and energy optimization [J]. IEEE transactions on wireless communications, 2018, 17(8): 5092-5106. DOI:10.1109/twc.2018.2838134 DOI
17	WU Q Q, ZENG Y, ZHANG R. Joint trajectory and communication design for multi⁃UAV enabled wireless networks [J]. IEEE transactions on wireless communications, 2018, 17(3): 2109-2121. DOI:10.1109/twc.2017.2789293 DOI
18	XIE L F, XU J, ZHANG R. Throughput maximization for UAV⁃enabled wireless powered communication networks [J]. IEEE Internet of Things journal, 2019, 6(2): 1690-1703. DOI:10.1109/jiot.2018.2875446 DOI
19	ZHOU F, WU Y, HU R Q, QIAN Y. Computation rate maximization in UAV⁃enabled wireless powered mobile⁃edge computing systems [J]. IEEE journal on selected areas in communications, 2018, 36(9): 1927-1941. DOI: 10.1109/JSAC.2018.2864426A DOI
20	GARG S, SINGH A, BATRA S, et al. UAV⁃empowered edge computing environment for cyber⁃threat detection in smart vehicles [J]. IEEE network, 2018, 32(3): 42-51. DOI:10.1109/mnet.2018.1700286 DOI
21	PU L J, CHEN X, MAO G Q, et al. Chimera: an energy⁃efficient and deadline⁃aware hybrid edge computing framework for vehicular crowdsensing applications [J]. IEEE Internet of Things journal, 2019, 6(1): 84-99. DOI:10.1109/jiot.2018.2872436 DOI
22	DU Y, WANG K Z, YANG K, et al. Energy⁃efficient resource allocation in UAV based MEC system for IoT devices [C]//2018 IEEE Global Communications Conference (GLOBECOM). Abu Dhabi, United Arab Emirates, 2018: 9-13. DOI:10.1109/glocom.2018.8647789 DOI
23	JEONG S, SIMEONE O, KANG J. Mobile edge computing via a UAV⁃mounted cloudlet: optimization of bit allocation and path planning [J]. IEEE transactions on vehicular technology, 2018, 67(3): 2049-2063. DOI:10.1109/tvt.2017.2706308 DOI
24	ZHOU F H, WU Y P, SUN H J, et al. UAV⁃enabled mobile edge computing: offloading optimization and trajectory design [C]//2018 IEEE International Conference On Communications (ICC). Kansas City, USA, 2018: 20-24. DOI:10.1109/icc.2018.8422277 DOI
25	DURISI G, KOCH T, POPOVSKI P. Toward massive, ultrareliable, and low⁃latency wireless communication with short packets [J]. Proceedings of the IEEE, 2016, 104(9): 1711-1726. DOI:10.1109/jproc.2016.2537298 DOI
26	SHEN C, CHANG T H, XU H Q, et al. Joint uplink and downlink transmission design for URLLC using finite blocklength codes [C]//2018 15th International Symposium on Wireless Communication Systems (ISWCS). Lisbon, Portugal, 2018: 28-31. DOI:10.1109/iswcs.2018.8491069 DOI
27	PAN C H, REN H, DENG Y S, et al. Joint blocklength and location optimization for URLLC⁃enabled UAV relay systems [J]. IEEE communications letters, 2019, 23(3): 498-501. DOI:10.1109/lcomm.2019.2894696 DOI
28	BOYD S, VANDENBERGHE L. Convex optimization [M]. Cambridge, U.K: Cambridge University Press, 2004. DOI:10.1017/cbo9780511804441 DOI
29	BOYD S. Convex optimization II [EB/OL]. (2013⁃09⁃11)[2019⁃10⁃20].

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Joint Placement and Resource Allocation for UAV-Assisted Mobile Edge Computing Networks with URLLC

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