TY - JOUR
T1 - Delay minimization for massive MIMO assisted mobile edge computing
AU - Zeng, Ming
AU - Zeng, Ming
AU - Hao, Wanming
AU - Dobre, Octavia A.
AU - Poor, H. Vincent
N1 - Funding Information:
Manuscript received July 28, 2019; revised January 20, 2020; accepted March 1, 2020. Date of publication March 9, 2020; date of current version June 18, 2020. This work was supported in part by the Natural Sciences and Engineering Research Council of Canada, through its Discovery Program and in part by the U.S. National Science Foundation under Grants CCF-0939370, CCF-1513915, and CCF-1908308. The review of this article was coordinated by Dr. J. Liu. (Corresponding author: Octavia Dobre.) Ming Zeng is with the Memorial University, St. John’s, NL A1B 3X9, Canada, and also with the Faculty of Science and Engineering, Laval University, Quebec, QC, G1V0A6, Canada (e-mail: mzeng@mun.ca).
Publisher Copyright:
© 1967-2012 IEEE.
PY - 2020/6
Y1 - 2020/6
N2 - Mobile edge computing (MEC) has been envisioned as a promising technology for enhancing the computational capacities of mobile devices, by enabling computational task offloading. In this article, we employ massive multiple-input multiple-output methods to facilitate offloading in MEC. Our objective is to minimize the maximum delay for offloading and computing among the users, which requires a joint allocation of wireless and computational resources. Both perfect and imperfect channel state information (CSI) are considered. Under perfect CSI, we derive a semi-closed-form solution for the formulated problem. Under imperfect CSI, since the formulated problem is non-convex, we transform it into a convex one using a successive convex approximation technique and propose an iterative algorithm to solve it. Presented numerical results show the benefits of having a large number of antennas at the base station, and the necessity of performing joint radio and computational resource allocation.
AB - Mobile edge computing (MEC) has been envisioned as a promising technology for enhancing the computational capacities of mobile devices, by enabling computational task offloading. In this article, we employ massive multiple-input multiple-output methods to facilitate offloading in MEC. Our objective is to minimize the maximum delay for offloading and computing among the users, which requires a joint allocation of wireless and computational resources. Both perfect and imperfect channel state information (CSI) are considered. Under perfect CSI, we derive a semi-closed-form solution for the formulated problem. Under imperfect CSI, since the formulated problem is non-convex, we transform it into a convex one using a successive convex approximation technique and propose an iterative algorithm to solve it. Presented numerical results show the benefits of having a large number of antennas at the base station, and the necessity of performing joint radio and computational resource allocation.
KW - Massive multiple-input multiple-output (MIMO)
KW - delay minimization
KW - joint resource allocation
KW - mobile edge computing (MEC)
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U2 - 10.1109/TVT.2020.2979434
DO - 10.1109/TVT.2020.2979434
M3 - Article
AN - SCOPUS:85086887057
SN - 0018-9545
VL - 69
SP - 6788
EP - 6792
JO - IEEE Transactions on Vehicular Communications
JF - IEEE Transactions on Vehicular Communications
IS - 6
M1 - 9027954
ER -