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Energy Optimization for Cellular-Connected Multi-UAV Mobile Edge Computing Systems with Multi-Access Schemes

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Journal of Communications and Information Networks

Abstract

In this paper, a cellular-connected unmanned aerial vehicle (UAV) mobile edge computing system is studied where several UAVs are associated to a terrestrial base station (TBS) for computation offloading. To compute the large amount of data bits, a part of computation task is migrated to TBS and the other part is locally handled at UAVs. Our goal is to minimize the total energy consumption of all UAVs by jointly adjusting the bit allocation, power allocation, resource partitioning as well as UAV trajectory under TBS’s energy budget. For deeply comprehending the impact of multi-UAV access strategy on the system performance, four access schemes in the uplink transmission is considered, i.e., time division multiple access, orthogonal frequency division multiple access, one-by-one access and non-orthogonal multiple access. The involved problems under different access schemes are all formulated in non-convex forms, which are difficult to be tackled optimally. To solve this class of problem, the successive convex approximation technique is employed to obtain the suboptimal solutions. The numerical results show that the proposed scheme save significant energy consumption compared with the benchmark schemes.

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Author information

Authors and Affiliations

  1. School of Information Science and Engineering, Southeast University, Nanjing, 210096, China

    Meng Hua, Yongming Huang & Lyuxi Yang

  2. School of Electronics and Communication Engineering, Zhengzhou University of Aeronautics, Zhengzhou, 450046, China

    Yi Wang

  3. National Digital Switching System Engineering and Technological Research Center, Zhengzhou, 450002, China

    Yi Wang

  4. Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore

    Qingqing Wu

  5. School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing, 210003, China

    Haibo Dai

Authors
  1. Meng Hua
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  2. Yongming Huang
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  3. Yi Wang
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  4. Qingqing Wu
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  5. Haibo Dai
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  6. Lyuxi Yang
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Corresponding author

Correspondence to Lyuxi Yang.

Additional information

This work was supported by National High Technology Project of China under Grant 2015AA01A703, Scientific and Technological Key Project of Henan Province under Grant 182102210449 and China Postdoctoral Science Foundation under Grant 2018M633733, the Scientific Key Research Project of Henan Province for Colleges and Universities under Grand 19A510024, the Scientific Research Foundation of Graduate School of Southeast University under Grand YBPY1859, the National Science and Technology Major Project of China under Grant 2018ZX03001002-003, and the Research Project of Jiangsu Province under Grant BE2018121, the Natural Science Foundation of the Jiangsu Higher Education Institutions of China under Grant 18KJB510026, and by the Foundation of Nanjing University of Posts and Telecommunications under Grant NY218124, the National Natural Science Foundation of China under Grants 61801243, 61801435, 61372101, 61720106003. The associate editor coordinating the review of this paper and approving it for publication was W. Zhang.

Meng Hua received his B.S. degree in electrical and information engineering from Huangshan University, Huangshan, China, in 2013, and M.S. degree in electrical and information engineering from Nanjing University of Science and Technology, Nanjing, China, in 2016. Since September 2016, he has been working towards his Ph.D. degree in information and communication engineering at the School of Information Science and Engineering, Southeast University, Nanjing, China. His current research interests include massive MIMO, energy-efficient wireless communication, UAV, mobile edge computing and optimization theory.

Yongming Huang (SM’17) received his B.S. and M.S. degrees from Nanjing University, Nanjing, China, in 2000 and 2003, respectively, and his Ph.D. degree in electrical engineering from Southeast University, Nanjing, in 2007. Since 2007, he has been a faculty member with the School of Information Science and Engineering, Southeast University. In 2008 and 2009, he visited the Signal Processing Laboratory, School of Electrical Engineering, Royal Institute of Technology (KTH), Stockholm, Sweden. His current research interests include MIMO wireless communications, cooperative wireless communications and millimeter wave wireless communications. He has published over 200 peer-reviewed papers, hold over 40 invention patents, and submitted over 10 technical contributions to IEEE standards. Since 2012, he has served as an associate editor for the IEEE Transactions on Signal Processing, EURASIP Journal on Advances in Signal Processing, and EURASIP Journal on Wireless Communications and Networking.

Yi Wang received his B.S. degree from PLA Information Engineering University, Zhengzhou, China, in 2006, and his M.S. and Ph.D. degrees from the School of Information Science and Engineering, Southeast University, China, in 2009 and 2016, respectively. Since October 2016, he has been with the School of Electronics and Communication Engineering, Zhengzhou University of Aeronautics, China. His current research interests include signal processing, energy-efficient communication, relay-aided system and massive MIMO. He received the best paper awards of the IEEE WCSP in 2015.

Qingqing Wu received his B.Eng. and Ph.D. degrees in electronic engineering from South China University of Technology and Shanghai Jiao Tong University (SJTU), China, in 2012 and 2016 (in advance), respectively. He is now a research fellow in National University of Singapore. He received the IEEE WCSP Best Paper Award in 2015, the Exemplary Reviewer of IEEE Communications Letters in 2016 and 2017, and the Exemplary Reviewer of IEEE Transactions on Communications and IEEE Transactions on Wireless Communications in 2017. He was the recipient of the Outstanding Ph.D. Thesis Funding in SJTU in 2016 and the Best Ph.D. Thesis Award of China Institute of Communications in 2017. He served as a TPC member of IEEE ICC, Globecom, WCNC, VTC, APCC, WCSP, etc. His research interests include intelligent reflecting surface, energy-efficient wireless communications, wireless power transfer, and unmanned aerial vehicle (UAV) communications.

Haibo Dai received his M.S. degree from Yanshan University, Qinhuangdao, China, in 2013, and his Ph.D. degree in electrical engineering from Southeast University, Nanjing, China, in 2017. Since February 2018, he has been a faculty member in the School of Internet of Things, Nanjing University of Posts and Telecommunications. His current research interests include wireless resource allocation and management, unmanned aerial vehicle communications, optimization in heterogeneous networks, game theory and machine learning in 5G networks and beyond. He has published many papers in international journals such as IEEE Journal on Selected Areas in Communications and IEEE Transactions on Vehicular Technology, as well as papers in conferences such as IEEE WCNC and IEEEWCSP.

Lyuxi Yang (S’04-M’04) [corresponding author] received his M.S. and Ph.D. degrees in electrical engineering from the Southeast University, Nanjing, China, in 1990 and 1993, respectively. Since 1993, he has been with the Department of Radio Engineering, Southeast University, where he is currently a full professor of information systems and communications, and the director of Digital Signal Processing Division. His current research interests include signal processing for wireless communications, MIMO communications, cooperative relaying systems, and statistical signal processing. He has authored or co-authored two published books and more than 100 journal papers, and holds 30 patents. Prof. Yang received the first and second class prizes of Science and Technology Progress Awards of the State Education Ministry of China in 1998, 2002 and 2014. He is currently a member of Signal Processing Committee of Chinese Institute of Electronics.

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Hua, M., Huang, Y., Wang, Y. et al. Energy Optimization for Cellular-Connected Multi-UAV Mobile Edge Computing Systems with Multi-Access Schemes. J. Commun. Inf. Netw. 3, 33–44 (2018). https://doi.org/10.1007/s41650-018-0035-0

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  • DOI: https://doi.org/10.1007/s41650-018-0035-0

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