Open AccessFlow Identification of Intelligent Wireless Communication Network Based on Optimal Symbol Output Control
Author(s) -
Wenwu Yu,
Ruijie Liu,
Yue Zhai,
Dan Wei
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1856/1/012038
Subject(s) - node (physics) , markov decision process , computer science , markov process , optimal decision , routing (electronic design automation) , wireless network , markov chain , identification (biology) , mathematical optimization , wireless , computer network , decision tree , data mining , mathematics , engineering , machine learning , telecommunications , statistics , botany , structural engineering , biology
Markov decision model is the most suitable decision algorithm for random dynamic system based on Markov process theory. Through its decision set at each node, the path that meets the requirements is selected as the allowed decision set for data forwarding, and it does not completely depend on the historical moment of the entire system. Since multiple paths may be included in the allowed decision set, further optimization is required to select the optimal path. In the paper, Markov routing decision model is applied to comprehensively consider the communication distance matrix, node transfer probability, and the number of node neighbors in the network. Equipped with different weights according to actual application scenarios, the nodes within the communication range are evaluated, and the evaluation results are used as the basis for the optimal symbol output to control the optimal path, thereby improving the identification of wireless communication network traffic, extending the network life cycle, and achieving global routing optimal strategy selection.