Research Article
Resource Optimization of Power Line Communication Network Based on Monte Carlo Method
@INPROCEEDINGS{10.1007/978-3-031-04245-4_18, author={Peiru Chen and Zhixiong Chen and Leixin Zhi and Lixia Zhang}, title={Resource Optimization of Power Line Communication Network Based on Monte Carlo Method}, proceedings={6GN for Future Wireless Networks. 4th EAI International Conference, 6GN 2021, Huizhou, China, October 30--31, 2021, Proceedings}, proceedings_a={6GN}, year={2022}, month={5}, keywords={Power line communication IEEE 1901 Resource optimization Monte Carlo}, doi={10.1007/978-3-031-04245-4_18} }- Peiru Chen
Zhixiong Chen
Leixin Zhi
Lixia Zhang
Year: 2022
Resource Optimization of Power Line Communication Network Based on Monte Carlo Method
6GN
Springer
DOI: 10.1007/978-3-031-04245-4_18
Abstract
The power system communication network uses power lines as a medium, which is an important means to ensure the safe, stable, and economic operation of the power grid. In order to improve the throughput of the power line communication (PLC) network and realize the optimization of network resources, a method based on the machine learning algorithm, namely the Monte Carlo method, is proposed to optimize the media access control (MAC) layer protocol of the PLC. Firstly, based on the lognormal fading and impulse noise in the physical layer of the PLC channel, and the IEEE 1901 CSMA backoff mechanism in the MAC layer, the main factors leading to packet loss are analyzed. Secondly, the throughput calculation model based on the above packet loss factors is established. Finally, according to the idea of Monte Carlo algorithm, the MAC layer contention window selection algorithm is established based on the principle of maximum throughput. And compared with the original algorithm standard simulation results, the effectiveness of the method is verified. The results show that the contention window value obtained based on the proposed algorithm can achieve higher system throughput, and has certain practical reference value in the corresponding PLC network scenario.
