A Power Allocation Algorithm in Vehicular Edge Computing Networks Based on Deep Reinforcement Learning

被引：0
|
作者：
Qiu B. [1 ,2 ]
Wang Y. [1 ]
Xiao H. [3 ]
机构：
[1] School of Information Science and Engineering, Guilin University of Technology, Guilin
[2] Guangxi Key Laboratory of Embedded Technology and Intelligent System, Guilin
[3] School of Computer Science and Information Engineering, Hubei University, Wuhan
来源：
Beijing Youdian Daxue Xuebao/Journal of Beijing University of Posts and Telecommunications | 2024年 / 47卷 / 02期
关键词：
computation offloading; deep deterministic policy gradient; power allocation; service caching; vehicular edge computing;
D O I：
10.13190/j.jbupt.2023-032
中图分类号：
学科分类号：
摘要：
To address the time-varying channel and stochastic task arrival problems caused by the mobility of vehicle in the vehicular edge computing environment, a deep reinforcement learning-based computation offloading and power allocation algorithm is proposed. First, we build a three-layer system model for end-edge-cloud orchestrated computing based on non-orthogonal multiple access in a two-way lane scenario. Then, by combining the communication, computing, cache resources and the mobility of vehicle, a joint optimization problem is designed to minimize the long-term cumulative total system cost consisting of power consumption and cache latency. Finally, considering the dynamics, time-varying and stochastic characteristics in vehicular edge computing networks, a decentralized intelligent algorithm based on deep deterministic policy gradient is proposed for obtaining the power allocation optimization. Compared with conventional baseline algorithms, the simulation results demonstrate that the proposed algorithm can achieve a superior performance in reducing the cost of the total system. © 2024 Beijing University of Posts and Telecommunications. All rights reserved.
引用
收藏
页码：81 / 89
页数：8
相关论文
共 9 条
  • [1] DU J B, YU F R, CHU X L, Et al., Computation offloading and resource allocation in vehicular networks based on dual-side cost minimization, IEEE Transactions on Vehicular Technology, 68, 2, pp. 1079-1092, (2019)
  • [2] KO H, KYUNG Y., Performance analysis and optimization of delayed offloading system with opportunistic fog node, IEEE Transactions on Vehicular Technology, 71, 9, pp. 10203-10208, (2022)
  • [3] QIAO G H, LENG S P, MAHARJAN S, Et al., Deep reinforcement learning for cooperative content caching in vehicular edge computing and networks, IEEE Internet of Things Journal, 7, 1, pp. 247-257, (2020)
  • [4] CAO D, ZHANG Y B, ZOU D, Et al., Multi-node cooperative distributed offloading strategy in V2X scenario, Journal on Communications, 43, 2, pp. 185-195, (2022)
  • [5] DONG P R, NING Z L, MA R, Et al., NOMA-based energy-efficient task scheduling in vehicular edge computing networks: a self-imitation learning-based approach, China Communications, 17, 11, pp. 1-11, (2020)
  • [6] KE H C, WANG J, DENG L Y, Et al., Deep reinforcement learning-based adaptive computation offloading for MEC in heterogeneous vehicular networks [J], IEEE Transactions on Vehicular Technology, 69, 7, pp. 7916-7929, (2020)
  • [7] ZHU H B, WU Q, WU X J, Et al., Decentralized power allocation for MIMO-NOMA vehicular edge computing based on deep reinforcement learning, IEEE Internet of Things Journal, 9, 14, pp. 12770-12782, (2022)
  • [8] WU Z Y, YAN D F., Deep reinforcement learning-based computation offloading for 5G vehicle-aware multi-access edge computing network [J], China Communications, 18, 11, pp. 26-41, (2021)
  • [9] ZHAN W H, LUO C B, WANG J, Et al., Deep-reinforcement-learning-based offloading scheduling for vehicular edge computing, IEEE Internet of Things Journal, 7, 6, pp. 5449-5465, (2020)
1