PLC for In-Vehicle Network: A DRL-Based Algorithm of Diversity Combination of OFDM Subcarriers

作     者：CHEN Zhixiong ZHANG Zhikun CAO Tianshu ZHOU Zhenyu CHEN Zhixiong;ZHANG Zhikun;CAO Tianshu;ZHOU Zhenyu

作者机构：School of Electrical and Electronic Engineering North China Electric Power University Hebei Key Laboratory of Power Internet of Things Technology 

出 版 物：《Chinese Journal of Electronics》 (电子学报(英文))

年 卷 期：2023年第32卷第6期

页      面：1245-1257页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 080204[工学-车辆工程] 0804[工学-仪器科学与技术] 080402[工学-测试计量技术及仪器] 0802[工学-机械工程] 080201[工学-机械制造及其自动化] 

基　　金：supported by the National Natural Science Foundation of China (61601182) the Fundamental Research Funds for the Central Universities (2021MS070) 

主　　题：Power line communication OFDM Low latency Diversity Deep reinforcement learning 

摘      要：For low latency communication service of vehicles, it is critical to improve the delay performance of power line communication(PLC) for in-vehicle network,which can decrease the weight and cost of the vehicle. In order to minimize the total time slots used in a transmission task, an orthogonal frequency-division multiplexing(OFDM) subcarrier diversity combination algorithm of PLC based on the deep reinforcement learning(DRL) is proposed herein. The short packet communication theory is used to develop an optimal combination model with constraints on short packet reliability, transmitting power and the amount of data. The state, action, and reward function of double deep Q-learning network(DDQN) are defined, and diversity combination for OFDM subcarriers is performed using DDQN. An adaptive power allocation algorithm based on the thresholds of error rate and the data amount is used. Simulation results show that the proposed algorithm can effectively improve the delay performance of PLC under the constraints of power and data amount.