A method (200) of reconfiguring a telecommunications network (100), the telecommunications network being configured to facilitate telehaptic communication with a User Equipment (UE) (110), the method comprising the steps of: processing a network communication so as to determine a likelihood of future telehaptic communication with the UE (230); determining that said likelihood of future telehaptic communication exceeds a threshold value (240); and in response to said determining, reconfiguring the telecommunications network so as to improve network performance for the UE (250).

A method (200) of reconfiguring a telecommunications network (100), the telecommunications network being configured to facilitate telehaptic communication with a User Equipment (UE) (110), the method comprising the steps of: processing a network communication so as to determine a likelihood of future telehaptic communication with the UE (230); determining that said likelihood of future telehaptic communication exceeds a threshold value (240); and in response to said determining, reconfiguring the telecommunications network so as to improve network performance for the UE (250).

The present invention relates to a location-awareness-based network intermediate device, at least comprising a scheduling module, which is configured to fuse short-term historical data about short-term and/or real-time network performance and location-awareness-based multi-day network performance data, so as to generate context data for predicting network performance, and acquire a deterministic connectivity mode in a mobile network environment based on the context data so as to enhance performance of end-to-end transmission. With this configuration, the concept of location-awareness-based packet scheduling can be translated into an actual system for end-to-end multi-path transmission. From the location-awareness-based long-term multi-day network performance data, the deterministic connectivity pattern of trains in the high-speed mobile network environment can be acquired, and after being fused with the short-term historical data observed in a short time period, a good basis for calibration of prediction of network performance can be provided, thereby enhancing robustness of network connectivity in a high-speed movement scene.

The present invention relates to a location-awareness-based network intermediate device, at least comprising a scheduling module, which is configured to fuse short-term historical data about short-term and/or real-time network performance and location-awareness-based multi-day network performance data, so as to generate context data for predicting network performance, and acquire a deterministic connectivity mode in a mobile network environment based on the context data so as to enhance performance of end-to-end transmission. With this configuration, the concept of location-awareness-based packet scheduling can be translated into an actual system for end-to-end multi-path transmission. From the location-awareness-based long-term multi-day network performance data, the deterministic connectivity pattern of trains in the high-speed mobile network environment can be acquired, and after being fused with the short-term historical data observed in a short time period, a good basis for calibration of prediction of network performance can be provided, thereby enhancing robustness of network connectivity in a high-speed movement scene.

[Object] To enhance a shared model in a case of predicting a wireless environment by machine learning securely and efficiently in terms of personal information protection.

[Solving Means] This information processing system includes: one or more information processing apparatuses including a first arithmetic processing unit that learns a model for predicting a wireless environment, uploads a result of the learning to a server, and predicts a wireless environment by using a shared model obtained by integrating one or more learning results in the server; and a server including a second arithmetic processing unit that integrates one or more learning results in the one or more information processing apparatuses and generates a shared model, and distributes the shared model to the one or more information processing apparatuses.

[Object] To enhance a shared model in a case of predicting a wireless environment by machine learning securely and efficiently in terms of personal information protection.

[Solving Means] This information processing system includes: one or more information processing apparatuses including a first arithmetic processing unit that learns a model for predicting a wireless environment, uploads a result of the learning to a server, and predicts a wireless environment by using a shared model obtained by integrating one or more learning results in the server; and a server including a second arithmetic processing unit that integrates one or more learning results in the one or more information processing apparatuses and generates a shared model, and distributes the shared model to the one or more information processing apparatuses.

Disclosed is a 5G or pre-5G communication system for supporting a data transmission rate higher than that of a 4G communication system such as LTE. A communication method of a terminal in a wireless communication system, which includes a first base station supporting first wireless communication and a second base station supporting second wireless communication, can comprise the steps of: performing data communication though the first wireless communication with the first base station; receiving, from the first or second base station, configuration information for second wireless communication connection; reporting, to the first or second base station, the result of measurement on at least one beamforming reference signal having been received from the second base station, on the basis of the configuration information; and establishing the second wireless communication connection with the second base station on the basis of the measurement result.

Disclosed is a 5G or pre-5G communication system for supporting a data transmission rate higher than that of a 4G communication system such as LTE. A communication method of a terminal in a wireless communication system, which includes a first base station supporting first wireless communication and a second base station supporting second wireless communication, can comprise the steps of: performing data communication though the first wireless communication with the first base station; receiving, from the first or second base station, configuration information for second wireless communication connection; reporting, to the first or second base station, the result of measurement on at least one beamforming reference signal having been received from the second base station, on the basis of the configuration information; and establishing the second wireless communication connection with the second base station on the basis of the measurement result.

A wireless communication network serves User Equipment (UE) responsive to an Artificial Intelligence (AI) network. The UE transfers UE data that indicates user applications and their current status to a distributed ledger. The distributed ledger also receives past quality levels and locations from the wireless communication network. The distributed ledger stores the UE data, quality levels, and locations in a blockchain format that is readable by the AI network. The distributed ledger receives a future quality level and location and time for the UE from the AI network. The distributed ledger stores the future quality level and location and time for the UE in the blockchain format. The distributed ledger transfers the future quality level and location and time for the UE to an Exposure Function (EF). The EF signals a network control-plane to deliver the wireless data service to the UE at the future location and time and quality level.

A wireless communication network serves User Equipment (UE) responsive to an Artificial Intelligence (AI) network. The UE transfers UE data that indicates user applications and their current status to a distributed ledger. The distributed ledger also receives past quality levels and locations from the wireless communication network. The distributed ledger stores the UE data, quality levels, and locations in a blockchain format that is readable by the AI network. The distributed ledger receives a future quality level and location and time for the UE from the AI network. The distributed ledger stores the future quality level and location and time for the UE in the blockchain format. The distributed ledger transfers the future quality level and location and time for the UE to an Exposure Function (EF). The EF signals a network control-plane to deliver the wireless data service to the UE at the future location and time and quality level.