计算机科学
边缘计算
强化学习
编码(社会科学)
移动边缘计算
分布式计算
车载自组网
雷达
实时计算
算法
计算机网络
GSM演进的增强数据速率
无线
人工智能
服务器
无线自组网
电信
统计
数学
作者
Hoai Linh Nguyen Thi,Hung Van Hoang,Nguyen Cong Luong,Tiến Hoa Nguyễn,Sa Xiao,Junjie Tan,Dusit Niyato
标识
DOI:10.1109/vtc2023-fall60731.2023.10333382
摘要
In this paper, we propose a coded distributed computing (CDC)-based vehicular edge computing (VEC) framework. The framework allows a task vehicle (TV) equipped with the dual-function radar communication (DFRC) to offload its computing tasks to the nearby service vehicles (SVs) using the (m, k) maximum distance separable (MDS). The framework is thus able to address the straggler effect that is typically caused by the high mobility of the vehicles. We then formulate an optimization problem for the TV that aims to i) minimize the overall computing latency, ii) minimize the offloading cost, and iii) maximize the radar range subject to the connection duration. For this, we optimize the MDS parameters, i.e., the number of selected SVs (m) and the number of subtasks for coding (k), and the fractions of power allocated to the radar and communication functions. Under the high dynamic vehicular environment, the uncertainty of the SVs’ computing resource and networking resources, we propose a deep reinforcement learning (DRL) algorithm based on Double Deep Q-Network (DDQN) to solve the TV’s problem. To further improve the performance, we propose to incorporate a parameter norm penalty in the loss function. Simulation results show that the proposed DDQN algorithm outperforms both the DQN algorithm and the non-learning algorithm in terms of computation latency, radar range, and offloading cost.
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