Open AccessApplication of LTE-U Technology in Train-Ground Communication of Urban Rail Transit
Author(s) -
CZ Song,
Liu Xj,
Liu Wz
Publication year - 2022
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 - 10.1088/1742-6596/2158/1/012002
Subject(s) - computer science , throughput , frequency allocation , interference (communication) , task (project management) , spectrum management , transit (satellite) , duty cycle , transmission (telecommunications) , markov decision process , scheme (mathematics) , partially observable markov decision process , markov process , computer network , markov chain , data transmission , range (aeronautics) , process (computing) , real time computing , markov model , telecommunications , wireless , engineering , channel (broadcasting) , public transport , cognitive radio , electrical engineering , transport engineering , mathematics , systems engineering , voltage , aerospace engineering , mathematical analysis , operating system , machine learning , statistics
With the development of intelligentizing of Urban Rail Transit (URT), the data that need to be transmitted between train and OCC (Operational Contral Center) has increased significantly, and the licensed spectrum range is gradually unable to satisfy the data transfer task of URT communication systems(URT-cs). Therefore, this article considers to use LTE-U (Long-Term-Evolution in unlicensed spectrum) technology to deploy this communication system in unlicensed spectrum. In view of the interference caused by the introduction to WLAN system which have already been deployed in unlicensed spectrum, a dynamic spectrum range duty cycle adjustment scheme based on Q-learning algorithm is proposed to achieve coexistence of the two system. The scheme takes spectrum range as state, modelling the spectrum range allocation as a Markov Decision Process, obtains the spectrum range allocation strategy through Q-learning algorithm. Simulation results show that, compared with fixed spectrum allocation, the proposed scheme has a higher throughput and user satisfaction. Realizing the transmission task of URT-cs while meeting the user satisfaction of the WLAN system, and the problem of spectrum resources insufficiency of URT-cs is well solved.