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

CHEN Zhixiong; ZHANG Zhikun; CAO Tianshu; ZHOU Zhenyu

doi:10.23919/cje.2022.00.331

Volume 32 Issue 6

Nov. 2023

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Article Navigation > Chinese Journal of Electronics > 2023 > 32(6): 1245-1257

CHEN Zhixiong, ZHANG Zhikun, CAO Tianshu, et al., “PLC for In-Vehicle Network: A DRL-Based Algorithm of Diversity Combination of OFDM Subcarriers,” Chinese Journal of Electronics, vol. 32, no. 6, pp. 1245-1257, 2023, doi: 10.23919/cje.2022.00.331

Citation:

CHEN Zhixiong, ZHANG Zhikun, CAO Tianshu, et al., “PLC for In-Vehicle Network: A DRL-Based Algorithm of Diversity Combination of OFDM Subcarriers,” Chinese Journal of Electronics, vol. 32, no. 6, pp. 1245-1257, 2023, doi: 10.23919/cje.2022.00.331

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

doi: 10.23919/cje.2022.00.331

1.
School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China
2.
School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China
3.
Hebei Key Laboratory of Power Internet of Things Technology, Baoding 071003, China

Funds: This work was supported by the National Natural Science Foundation of China (61601182) and the Fundamental Research Funds for the Central Universities (2021MS070)

More Information

Author Bio:
Zhixiong CHEN was born in 1983. He received the M.S. degree from Harbin Institute of Technology of China in 2007 and the Ph.D. degree in electrical engineering and its automation from North China Electric Power University of China in 2010. He is an Associate Professor at School of Electrical and Electronic Engineering, North China Electric Power University, China. His research interests include power line communications, smart grid communications and Internet of things. (Email: zxchen@ncepu.edu.cn)

Zhikun ZHANG (corresponding author) was born in 1995. He received the B.E. degree in communication engineering from North China Electric Power University in 2020. He is pursuing the M.S. candidate in information and communication engineering from North China Electric Power University. His current research interests include power line and wireless hybrid Communication.(Email: zhikun_zhang@126.com)

Tianshu CAO was born in 1997. She received the B.E. degree in communication engineering from North China Electric Power University in 2021. She is currently pursuing the M.S. candidate in information and communication engineering from North China Electric Power University. Her current research interests include power line and wireless hybrid communication. (Email: 201703000401@ncepu.edu.cn)

Zhenyu ZHOU was born in 1983. He received the M.S. degree and Ph.D. degree from Waseda University, Tokyo, Japan in 2008 and 2011 respectively. From September 2012 to April 2019, he was an Associate Professor at School of Electrical and Electronic Engineering, North China Electric Power University, China. Since April 2019, he has been a Full Professor at the same university. His research interests mainly focus on resource allocation in device-to-device (D2D) communications, machine-to-machine (M2M) communications, smart grid communications, and Internet of things (IoT). He is a Senior Member of IEEE, Chinese Institute of Electronics (CIE), and China Institute of Communications (CIC). (Email: zhenyu_zhou@ncepu.edu.cn)
Received Date: 2022-09-29
Accepted Date: 2023-04-25

Available Online: 2023-05-23

Publish Date: 2023-11-05

Abstract

Abstract

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.
- Power line communication,
- OFDM,
- Low latency,
- Diversity,
- Deep reinforcement learning

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References(30)

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