Latency minimization for mmWave D2D mobile edge computing systems: joint task allocation and hybrid beamforming design
Latency minimization for mmWave D2D mobile edge computing systems: joint task allocation and hybrid beamforming design
Mobile edge computing (MEC) and millimeter wave (mmWave) communications are capable of significantly reducing the network’s delay and enhancing its capacity. In this paper we investigate a mmWave and device-to-device (D2D) assisted MEC system, in which user A carries out some computational tasks and shares the results with user B with the aid of a base station (BS). We assume partial offloading model and the task can be partitioned into two portions: the first part is computed locally at user A, while the second part is transmitted to the BS and computed by the MEC server. The computational results are then sent to user B through a D2D link and via the link from the BS to user B, respectively. To support computation offloading, both the users and the BS are equipped with multiple antennas and employ analog and digital (A/D) hybrid beamforming. Moreover, we propose a novel two-timescale joint hybrid beamforming and task allocation algorithm to reduce the system latency whilst cut down the required signaling overhead. Specifically, the highdimensional analog beamforming matrices are updated in a frame-based manner based on the channel state information (CSI) samples, where each frame consists of a number of time
slots, while the low-dimensional digital beamforming matrices and the offloading ratio are optimized more frequently relied on the low-dimensional effective channel matrices in each time slot. A stochastic successive convex approximation (SSCA) based algorithm is developed to design the long-term analog beamforming matrices. As for the short-term variables, the digital beamforming matrices are optimized relying on the innovative penalty-concave convex procedure (penalty-CCCP) for handling the mmWave non-linear transmit power constraint, and the offloading ratio can be obtained via the derived closed-form solution. Simulation results verify the effectiveness of the proposed algorithm by comparing the benchmarks.
Array signal processing, Computational modeling, D2D, Device-to-device communication, Mobile edge computing, Resource management, Servers, Signal processing algorithms, Task analysis, latency minimization, mmWave
12206-12221
Liu, Yanzhen
56fd81a3-15a3-4e11-a928-6e61f59f0311
Cai, Yunlong
ed1440c3-10af-4b6c-9295-4b355d409a16
Liu, An
9d011fff-353e-4dea-9161-ae1f323c8c4e
Zhao, Minjian
d552c3c1-57fd-4643-b914-4572ef291edf
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
1 November 2022
Liu, Yanzhen
56fd81a3-15a3-4e11-a928-6e61f59f0311
Cai, Yunlong
ed1440c3-10af-4b6c-9295-4b355d409a16
Liu, An
9d011fff-353e-4dea-9161-ae1f323c8c4e
Zhao, Minjian
d552c3c1-57fd-4643-b914-4572ef291edf
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
Liu, Yanzhen, Cai, Yunlong, Liu, An, Zhao, Minjian and Hanzo, Lajos
(2022)
Latency minimization for mmWave D2D mobile edge computing systems: joint task allocation and hybrid beamforming design.
IEEE Transactions on Vehicular Technology, 71 (11), .
(doi:10.1109/TVT.2022.3192345).
Abstract
Mobile edge computing (MEC) and millimeter wave (mmWave) communications are capable of significantly reducing the network’s delay and enhancing its capacity. In this paper we investigate a mmWave and device-to-device (D2D) assisted MEC system, in which user A carries out some computational tasks and shares the results with user B with the aid of a base station (BS). We assume partial offloading model and the task can be partitioned into two portions: the first part is computed locally at user A, while the second part is transmitted to the BS and computed by the MEC server. The computational results are then sent to user B through a D2D link and via the link from the BS to user B, respectively. To support computation offloading, both the users and the BS are equipped with multiple antennas and employ analog and digital (A/D) hybrid beamforming. Moreover, we propose a novel two-timescale joint hybrid beamforming and task allocation algorithm to reduce the system latency whilst cut down the required signaling overhead. Specifically, the highdimensional analog beamforming matrices are updated in a frame-based manner based on the channel state information (CSI) samples, where each frame consists of a number of time
slots, while the low-dimensional digital beamforming matrices and the offloading ratio are optimized more frequently relied on the low-dimensional effective channel matrices in each time slot. A stochastic successive convex approximation (SSCA) based algorithm is developed to design the long-term analog beamforming matrices. As for the short-term variables, the digital beamforming matrices are optimized relying on the innovative penalty-concave convex procedure (penalty-CCCP) for handling the mmWave non-linear transmit power constraint, and the offloading ratio can be obtained via the derived closed-form solution. Simulation results verify the effectiveness of the proposed algorithm by comparing the benchmarks.
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manuscript_final
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More information
Accepted/In Press date: 12 July 2022
e-pub ahead of print date: 19 July 2022
Published date: 1 November 2022
Additional Information:
Funding Information:
The work of Yunlong Cai was supported in part by the National Natural Science Foundation of China under Grants 61971376 and 61831004, and in part by the Zhejiang Provincial Natural Science Foundation for Distinguished Young Scholars under Grant LR19F010002. The work of Lajos Hanzowas supported in part by the Engineering and Physical Sciences Research Council projects under Grants EP/P034284/1 and EP/P003990/1 (COALESCE) and in part by the European Research Council’s Advanced Fellow Grant Quant-Com under Grant 789028
Publisher Copyright:
© 1967-2012 IEEE.
Keywords:
Array signal processing, Computational modeling, D2D, Device-to-device communication, Mobile edge computing, Resource management, Servers, Signal processing algorithms, Task analysis, latency minimization, mmWave
Identifiers
Local EPrints ID: 468523
URI: http://eprints.soton.ac.uk/id/eprint/468523
ISSN: 0018-9545
PURE UUID: cb8aadf3-4080-4d2c-b4a1-9c29c4d45485
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Date deposited: 17 Aug 2022 16:32
Last modified: 18 Mar 2024 02:36
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Contributors
Author:
Yanzhen Liu
Author:
Yunlong Cai
Author:
An Liu
Author:
Minjian Zhao
Author:
Lajos Hanzo
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