[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Ali et al., 2023 - Google Patents

Overview of Using Signaling Data from Radio Interface with Machine Learning Approaches

Ali et al., 2023

Document ID
13253286680720427138
Author
Ali H
Bestak R
Publication year
Publication venue
2023 International Conference on Military Technologies (ICMT)

External Links

Snippet

Mobile networks technologies are evolving rapidly in parallel with smart mobile devices wide spreading. On other hand, utilization of Artificial Intelligence in mobile networks has been increasing widely. It starts from mobile phones applications to mobile network …
Continue reading at ieeexplore.ieee.org (other versions)

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATIONS NETWORKS
    • H04W36/00—Hand-off or reselection arrangements
    • H04W36/24—Reselection being triggered by specific parameters used to improve the performance of a single terminal
    • H04W36/30—Reselection being triggered by specific parameters used to improve the performance of a single terminal by measured or perceived connection quality data
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATIONS NETWORKS
    • H04W28/00—Network traffic or resource management
    • H04W28/02—Traffic management, e.g. flow control or congestion control
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATIONS NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/08—Testing, supervising or monitoring using real traffic
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATIONS NETWORKS
    • H04W36/00—Hand-off or reselection arrangements
    • H04W36/0005—Control or signalling for completing the hand-off
    • H04W36/0083—Determination of parameters used for hand-off, e.g. generation or modification of neighbour cell lists
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATIONS NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/22—Traffic simulation tools or models
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATIONS NETWORKS
    • H04W4/00—Mobile application services or facilities specially adapted for wireless communication networks
    • H04W4/02—Mobile application Services making use of the location of users or terminals, e.g. OMA SUPL, OMA MLP or 3GPP LCS
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATIONS NETWORKS
    • H04W84/00—Network topologies
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATIONS NETWORKS
    • H04W72/00—Local resource management, e.g. wireless traffic scheduling or selection or allocation of wireless resources
    • H04W72/04—Wireless resource allocation

Similar Documents

Publication Publication Date Title
Kulkarni et al. Deepchannel: Wireless channel quality prediction using deep learning
US20220190883A1 (en) Beam prediction for wireless networks
Nguyen et al. Transfer learning for future wireless networks: A comprehensive survey
Nguyen et al. Transfer learning for wireless networks: A comprehensive survey
EP3972339A1 (en) Handover success rate prediction and management using machine learning for 5g networks
Khunteta et al. Deep learning based link failure mitigation
US11641244B2 (en) Method, apparatus and computer program for predicting a future quality of service of a wireless communication link
CN113498095B (en) Methods, apparatus and computer program products for predicting future quality of service of a wireless communication link
Boutiba et al. Radio link failure prediction in 5G networks
Manalastas et al. A data-driven framework for inter-frequency handover failure prediction and mitigation
Moysen et al. On the potential of ensemble regression techniques for future mobile network planning
Kaur et al. An efficient handover mechanism for 5G networks using hybridization of LSTM and SVM
Koursioumpas et al. AI-driven, context-aware profiling for 5G and beyond networks
Li et al. Machine learning based time domain millimeter-wave beam prediction for 5G-advanced and beyond: design, analysis, and over-the-air experiments
Jere et al. Bayesian Inference-assisted Machine Learning for Near Real-Time Jamming Detection and Classification in 5G New Radio (NR)
Ali et al. Overview of Using Signaling Data from Radio Interface with Machine Learning Approaches
Vielhaus et al. Handover predictions as an enabler for anticipatory service adaptations in next-generation cellular networks
Goudarzi et al. A novel model on curve fitting and particle swarm optimization for vertical handover in heterogeneous wireless networks
Gautam et al. Machine Learning Algorithms for 5G and Internet-of-Thing (IoT) Networks
Diouf et al. Channel quality prediction in 5G LTE small cell mobile network using deep learning
Magoula et al. A deep learning approach for distributed qos prediction in beyond 5G networks
Abdollahi et al. IODnet: Indoor/Outdoor Telecommunication Signal Detection through Deep Neural Network
Ferreira et al. Improving mmWave backhaul reliability: A machine-learning based approach
Morel et al. Improving User Environment Detection Using Context-aware Multi-Task Deep Learning in Mobile Networks
Lima et al. User-level handover decision making based on machine learning approaches