4.7 Article

Stackelberg Game of Energy Consumption and Latency in MEC Systems With NOMA

期刊

IEEE TRANSACTIONS ON COMMUNICATIONS
卷 69, 期 4, 页码 2191-2206

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCOMM.2021.3049356

关键词

Task analysis; Servers; Energy consumption; NOMA; Games; Resource management; Optimization; Mobile edge computing (MEC); non-orthogonal multiple access (NOMA); computational resource allocation; power allocation; task assignment; Stackelberg game

资金

  1. UK Engineering and Physical Sciences Research Council (EPSRC) [EP/P009719/2]
  2. EPSRC [EP/P009719/2] Funding Source: UKRI

向作者/读者索取更多资源

This article investigates a two-user scenario of a NOMA-based MEC network and formulates a Stackelberg game with a leader-follower relationship. Simulation results show that the closed-form solutions and user pairing algorithm significantly improve energy efficiency, and different task assignment strategies can be dynamically implemented.
In this article, a two-user scenario of a non-orthogonal multiple access (NOMA)-based mobile edge computing (MEC) network is investigated. By treating the users and the MEC server as leader and follower, respectively, a Stackelberg game is formulated. More specifically, the leader tends to minimize the total energy consumption for task offloading and local computing by optimizing the task assignment coefficients and transmit power. On the other side, the follower aims to minimize the total execution time by allocating different computational resources for processing the offloaded tasks. In order to solve the formulated problem, the Stackelberg equilibrium is considered. Based on the given insights, a closed-form solution of the follower level problem is obtained and included in the leader level one. Furthermore, by analyzing the leader's strategies, the leader level problem is solved through the Karush-Kuhn-Tucker (KKT) conditions, and closed-form expressions for the optimal task assignment coefficients and offloading time, are derived. Finally, this work is extended to the multi-user scenario, where a matching-based user pairing algorithm is proposed to assign users into different sub-channels. Simulation results indicate that: i) the derived closed-form solutions and the proposed user pairing algorithm can significantly improve energy efficiency; ii) the different task assignment strategies can be dynamically implemented to handle the varying wireless environment.

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