Open AccessAn Algorithmic Study to Maximize 5G Network Throughput Based on the Markov Decision Process
Author(s) -
Zheng Xiao
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 - 10.1088/1742-6596/1746/1/012051
Subject(s) - throughput , selection (genetic algorithm) , markov decision process , computer science , mathematical optimization , process (computing) , mode (computer interface) , markov process , markov chain , markov model , partially observable markov decision process , artificial intelligence , machine learning , mathematics , operating system , telecommunications , statistics , wireless
In this article, the network throughput optimization problem is investigated based on the theory of Markov decision process, combining the device-to-device direct selection problem with the finite stage discount MDP model problem. First, models for the device-to-device communication selection using MDP are built; second, the optimal mode selection strategy is derived using a finite stage backward iterative algorithm; and finally, the given mode selection strategy is evaluated by conducting a large number of simulation experiments. The results show that the MDP-based mode selection method proposed in this article has better performance in maximizing throughput and can yield better mode selection strategies with the advantage of obtaining larger system throughput.