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Dynamic Computation Offloading in Ultra-Dense Networks Based on Mean Field Games
IEEE Transactions on Wireless Communications ( IF 10.4 ) Pub Date : 2021-04-29 , DOI: 10.1109/twc.2021.3075028
Renjun Zheng , Haibo Wang , Matthieu De Mari , Miao Cui , Xiaoli Chu , Tony Q. S. Quek

In ultra-dense networks, the increasing popularity of computation intensive applications imposes challenges to the resource-constrained smart mobile devices (SMDs), which may be solved by offloading these computation tasks to the nearby mobile edge computing centers. However, when massive SMDs offload computation tasks in a dynamic wireless environment simultaneously, the joint optimization of their offloading decisions becomes prohibitively complex. In this paper, we firstly model the joint optimization problem as a multi-user non-cooperative dynamic stochastic game, then propose a mean field game based algorithm to solve it with a drastically reduced complexity. We derive the two partial differential equations ruling the optimal strategies of the mean field game, namely the Hamilton-Jacobi-Bellman and Fokker-Planck-Kolmogorov equations, which are solved in an iterative manner in our proposed algorithm. Numerical results demonstrate that the proposed mean field game-based offloading algorithm requires a lower cumulated cost than the conventional strategies under the latency constraints of computation tasks, with perfect prediction of future channel states. It also appears that the performance of the mean field game-based offloading strategy depends on the accuracy of the future channel knowledge provided to the system, as the uncertainty may compromise its cumulated cost performance.

中文翻译:

基于平均场博弈的超密集网络动态计算卸载

在超密集网络中,计算密集型应用程序的日益普及给资源受限的智能移动设备 (SMD) 带来了挑战,这可以通过将这些计算任务卸载到附近的移动边缘计算中心来解决。然而,当大量 SMD 同时卸载动态无线环境中的计算任务时,它们卸载决策的联合优化变得异常复杂。在本文中,我们首先将联合优化问题建模为多用户非合作动态随机博弈,然后提出一种基于平均场博弈的算法来解决它,并大大降低了复杂度。我们推导出决定平均场博弈最优策略的两个偏微分方程,即 Hamilton-Jacobi-Bellman 和 Fokker-Planck-Kolmogorov 方程,在我们提出的算法中以迭代方式解决。数值结果表明,在计算任务的延迟约束下,所提出的基于平均场博弈的卸载算法需要比传统策略更低的累积成本,并且可以完美地预测未来的通道状态。似乎基于平均场游戏的卸载策略的性能取决于提供给系统的未来通道知识的准确性,因为不确定性可能会损害其累积成本性能。对未来通道状态的完美预测。似乎基于平均场游戏的卸载策略的性能取决于提供给系统的未来通道知识的准确性,因为不确定性可能会损害其累积成本性能。对未来通道状态的完美预测。似乎基于平均场游戏的卸载策略的性能取决于提供给系统的未来通道知识的准确性,因为不确定性可能会损害其累积成本性能。
更新日期:2021-04-29
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