Mobile edge computing meets mmWave communications: joint beamforming and resource allocation for system delay minimization
Mobile edge computing meets mmWave communications: joint beamforming and resource allocation for system delay minimization
Mobile edge computing (MEC) has been identified as a key technique of next-generation wireless networks, which supports cloud computing along with other compelling service capabilities at the network’s edge with the objective of reducing the system delay. As one of the prospective candidates for new spectrum in next-generation networks, millimeter wave (mmWave) communications has been gaining significant attention as a benefit of its high rate. Hence we conceive a joint hybrid beamforming and resource allocation algorithm for mmWave MEC. Explicitly, we jointly optimize the analog beamforming vectors at the users, the analog and digital beamforming matrices at the base station (BS), the computation task offloading ratios
and resource allocation at the MEC server for minimizing the maximum system delay subject to the affordable communication and computing budget. We conceive a powerful algorithm for solving this challenging nonconvex optimization problem with coupled constraints based on the penalty dual decomposition (PDD) technique. The proposed algorithm can be implemented in a parallel and distributed fashion. Our numerical results demonstrate the superiority of the proposed algorithm by quantifying the benefits of intrinsically amalgamating MEC with mmWave communications.
Millimeter wave, distributed implementation, hybrid beamforming, mobile edge computing, resource allocation
2382-2396
Zhao, Cunzhuo
40581e16-8fe5-4de2-8a27-687f8380e556
Cai, Yunlong
ed1440c3-10af-4b6c-9295-4b355d409a16
Liu, An
2cfd49c6-be86-4acc-9275-e673372fe484
Zhao, Minjian
dda423b7-2472-42a1-a9bf-d1f973edddf9
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
1 April 2020
Zhao, Cunzhuo
40581e16-8fe5-4de2-8a27-687f8380e556
Cai, Yunlong
ed1440c3-10af-4b6c-9295-4b355d409a16
Liu, An
2cfd49c6-be86-4acc-9275-e673372fe484
Zhao, Minjian
dda423b7-2472-42a1-a9bf-d1f973edddf9
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
Zhao, Cunzhuo, Cai, Yunlong, Liu, An, Zhao, Minjian and Hanzo, Lajos
(2020)
Mobile edge computing meets mmWave communications: joint beamforming and resource allocation for system delay minimization.
IEEE Transactions on Wireless Communications, 19 (4), , [8959381].
(doi:10.1109/TWC.2020.2964543).
Abstract
Mobile edge computing (MEC) has been identified as a key technique of next-generation wireless networks, which supports cloud computing along with other compelling service capabilities at the network’s edge with the objective of reducing the system delay. As one of the prospective candidates for new spectrum in next-generation networks, millimeter wave (mmWave) communications has been gaining significant attention as a benefit of its high rate. Hence we conceive a joint hybrid beamforming and resource allocation algorithm for mmWave MEC. Explicitly, we jointly optimize the analog beamforming vectors at the users, the analog and digital beamforming matrices at the base station (BS), the computation task offloading ratios
and resource allocation at the MEC server for minimizing the maximum system delay subject to the affordable communication and computing budget. We conceive a powerful algorithm for solving this challenging nonconvex optimization problem with coupled constraints based on the penalty dual decomposition (PDD) technique. The proposed algorithm can be implemented in a parallel and distributed fashion. Our numerical results demonstrate the superiority of the proposed algorithm by quantifying the benefits of intrinsically amalgamating MEC with mmWave communications.
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- Accepted Manuscript
More information
Accepted/In Press date: 30 December 2019
e-pub ahead of print date: 14 January 2020
Published date: 1 April 2020
Additional Information:
Funding Information:
Manuscript received April 7, 2019; revised September 19, 2019 and December 20, 2019; accepted December 30, 2019. Date of publication January 14, 2020; date of current version April 9, 2020. The work of Yunlong Cai was supported in part by the National Natural Science Foundation of China under Grant 61831004 and Grant 61971376, and in part by the Zhejiang Provincial Natural Science Foundation for Distinguished Young Scholars under Grant LR19F010002. The work of Lajos Hanzo was supported in part by the Engineering and Physical Sciences Research Council under Project EP/Noo4558/1 and Project EP/PO34284/1, in part by the COALESCE, in part by the Royal Society’s Global Challenges Research Fund, and in part by the European Research Council’s Advanced Fellowship under Grant QuantCom. The associate editor coordinating the review of this article and approving it for publication was J. Lee. (Corresponding authors: Yunlong Cai; Minjian Zhao.) Cunzhuo Zhao, Yunlong Cai, An Liu, and Minjian Zhao are with the College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China (e-mail: zhaocz@zju.edu.cn; ylcai@zju.edu.cn; anliu@zju.edu.cn; mjzhao@zju.edu.cn).
Publisher Copyright:
© 2002-2012 IEEE.
Keywords:
Millimeter wave, distributed implementation, hybrid beamforming, mobile edge computing, resource allocation
Identifiers
Local EPrints ID: 436870
URI: http://eprints.soton.ac.uk/id/eprint/436870
ISSN: 1536-1276
PURE UUID: 2b0928a2-d2d4-4d17-8c87-805941b18bcf
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Date deposited: 13 Jan 2020 17:30
Last modified: 18 Mar 2024 05:15
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Contributors
Author:
Cunzhuo Zhao
Author:
Yunlong Cai
Author:
An Liu
Author:
Minjian Zhao
Author:
Lajos Hanzo
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