OPTIMIZATION OF INTELLIGENT TELECOMMUNICATION NETWORKS
نویسندگان
چکیده
The paper presents the results of research on use machine learning in telecommunication networks and describes basics theory artificial intelligence. impact dynamic Bayesian network (DBN) DNN development many technologies, including user activity detection, channel estimation, mobility tracking, is determined. indicators effectiveness communications based information bottlenecks, which at junction forecasting, statistics theory, are considered. A neural model that pretrained for high-level tasks divided into transmitter-side receiver-side uses investigated. process model, performed after its adjustment, taking ino account existing transmission channels, New ANN techniques capable predicting or adapting to sudden changes a wireless network, such as federated multiagent reinforcement (MARL), reviewed. DBN system dynamically develops over time, studied. considered provides constant monitoring work updating prediction behavior. Distributed forecasting states locations key component reliable communication systems possibility increasing number degrees freedom generalized G(E) terms of: physical propagation channel, directional diagram antenna array mutual influence, electromagnetic characteristics substantiated. ultra-highresolution localization algorithms, compressed sampling, imaging also identified. Mathematical expressions optimizing functional 5G/6G radio presented using new, sufficiently formal same time universal mathematical tools with an emphasis deep allow systematic, interpretable analysis large random wide range their models practical networks.
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ژورنال
عنوان ژورنال: Vìsnik Hmel?nic?kogo nacìonal?nogo unìversitetu
سال: 2023
ISSN: ['2307-5732']
DOI: https://doi.org/10.31891/2307-5732-2023-317-1-33-41