A Distributed Algorithm for Transmit Power Optimization in Mobile Edge Computing

doi:10.7652/xjtuxb201812018

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A Distributed Algorithm for Transmit Power Optimization in Mobile Edge Computing

更新时间：2025-05-08

- A Distributed Algorithm for Transmit Power Optimization in Mobile Edge Computing
- Vol. 52, Issue 12, Pages: 121-127(2018)
- 作者机构：
  
  北京交通大学计算机与信息技术学院,北京,100044
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb201812018
  CLC： TN915.6
- Online First：10 December 2018，
  
  Published：2018
- 稿件说明：
移动端阅览
A Distributed Algorithm for Transmit Power Optimization in Mobile Edge Computing[J]. 2018, 52(12): 121-127.
DOI：

A Distributed Algorithm for Transmit Power Optimization in Mobile Edge Computing[J]. 2018, 52(12): 121-127. DOI： 10.7652/xjtuxb201812018.

摘要

针对大规模移动边缘计算网络架构中的用户设备计算卸载时所需的通信和计算资源难以协同优化的问题

提出了一种基于马尔可夫近似的分布式发射功率优化算法。基于香农定理和链路传输特性

将用户功率最小化策略建模成组合优化模型

通过Log-Sum-Exp函数将目标模型转化为最小权重配置的近似问题; 针对该近似问题

提出了马尔可夫状态跳转的规则和分布式的设备自调节机制以实现高效求解。实验结果表明:与随机优化算法相比

该算法的系统用户设备发射总功率优化效果提升了78.5%

在给定场景下

穷举搜索最优解的计算复杂度可达4

而该算法仅需要迭代优化130次即可逼近最优解

能够有效减少通信和计算时延

确保发射功率的调整结果快速向最优目标收敛。

Abstract

A novel distributed algorithm for transmit power optimization based on Markov approximation framework is proposed to solve the problem that it is difficult to collaboratively optimize communication and computing resources for computing task offloading of devices in a large-scale mobile edge computing's network architecture. Strategy for minimizing power consumption of user devices is modeled as a combinatorial optimization model based on Shannon's theorem and link transmission characteristics

and then the problem is transformed into an approximation problem for minimum weight configuration by the Log-Sum-Exp approximation rule.Then

the approximation problem is efficiently solved by proposing the Markov state transition rule and the distributed self-adjustment mechanism. Experimental re

sults show that the optimized result of total power consumption of the devices by using the proposed algorithm is 78.5% better than that by using the random method. The proposed algorithm approaches the optimal solution after only 130 iterations in a given scenario

while the complexity of the exhaustive search's optimal solution is up to 4

. The proposed algorithm effectively reduces the delay and ensures that the adjustment result of the transmission power quickly converges to the optimal target.

关键词

Keywords

references

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