Distributed Power Control for Interference-Aware Multi-User Mobile Edge Computing: A Game Theory Approach | Zendy

Ning Li | Zendy; Jose-Fernan Martinez-Ortega | Zendy; Vicente Hernandez Diaz | Zendy

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Distributed Power Control for Interference-Aware Multi-User Mobile Edge Computing: A Game Theory Approach

Author(s) -

Ning Li,

Jose-Fernan Martinez-Ortega,

Vicente Hernandez Diaz

Publication year - 2018

Publication title -

ieee access

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.587

H-Index - 127

ISSN - 2169-3536

DOI - 10.1109/access.2018.2849207

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

The computation task offloading and resource management in mobile edge computing (MEC) become attractive in recent years. Many algorithms have been proposed to improve the performance of the MEC system. However, the research on power control in MEC systems is just starting. The power control in the single-user and an interference-free multi-user MEC systems has been investigated; but in the interference-aware multi-user MEC systems, this issue has not been learned in detail. Therefore, a game theory-based power control approach for the interference-aware multi-user MEC system is proposed in this paper. In this algorithm, both the interference and the multi-user scenario are considered. Moreover, the existence and uniqueness of the Nash Equilibrium (NE) of this game are proved, and the performance of this algorithm is evaluated via theoretical analysis and numerical simulation. The convergence, the computation complexity and the price of anarchy in terms of the system-wide computation overhead are investigated in detail. The performance of this algorithm has been compared with the traditional localized optimal algorithm by simulation. The simulation results demonstrate that the proposed algorithm has more advantages than the traditional one.

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