期刊
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
卷 19, 期 4, 页码 2382-2396出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TWC.2020.2964543
关键词
Millimeter wave; hybrid beamforming; mobile edge computing; resource allocation; distributed implementation
资金
- National Natural Science Foundation of China [61831004, 61971376]
- Zhejiang Provincial Natural Science Foundation for Distinguished Young Scholars [LR19F010002]
- Engineering and Physical Sciences Research Council [EP/Noo4558/1, EP/PO34284/1]
- COALESCE
- Royal Society's Global Challenges Research Fund
- European Research Council's Advanced Fellowship under Grant QuantCom
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 & x2019;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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