In a connected vehicle environment, network connection parameters such as a network congestion window and bit rate are automatically adjusted dependent on a location of a vehicle in order to optimize network performance. A geospatial database stores learned relationships between network performance of a connected vehicle at different physical locations when configured in accordance with different network parameters. The vehicle can then adjust its network parameters dynamically dependent on its location. A vehicle may maintain multiple connections to different networks concurrently for transmitting duplicate data of a data stream, with the vehicle independently adjusting parameters associated with different networks to optimize performance.

Concepts and technologies disclosed herein are directed to computer vision-based dynamic radio frequency (“RF”) planning and optimization. According to one aspect disclosed herein, a system can monitor cell site data from a cell site. The system can determine that the cell site data represents a change to the cell site. In response to determining that the cell site data represents the change to the cell site, the system can update clutter data associated with the cell site to reflect the change. The system can determine a potential RF signal attenuation associated with the object. The system can then determine that the potential RF signal attenuation associated with the object meets or exceeds a threshold. The system can trigger a remedial action to mitigate the potential RF signal attenuation.

Concepts and technologies disclosed herein are directed to computer vision-based dynamic radio frequency (“RF”) planning and optimization. According to one aspect disclosed herein, a system can monitor cell site data from a cell site. The system can determine that the cell site data represents a change to the cell site. In response to determining that the cell site data represents the change to the cell site, the system can update clutter data associated with the cell site to reflect the change. The system can determine a potential RF signal attenuation associated with the object. The system can then determine that the potential RF signal attenuation associated with the object meets or exceeds a threshold. The system can trigger a remedial action to mitigate the potential RF signal attenuation.

The present disclosure relates to a 5th generation (5G) or pre-5G communication system for supporting a data transmission rate higher than that of a 4th generation (4G) communication system such as long term evolution (LTE). The present disclosure is to generate environment information for a network design, and a method for generating environment information may include determining a location difference value of a building between first information based on photographing and second information based on measurement, and correcting a location of an obstacle determined using the first information using the location difference value.

The present disclosure relates to a 5th generation (5G) or pre-5G communication system for supporting a data transmission rate higher than that of a 4th generation (4G) communication system such as long term evolution (LTE). The present disclosure is to generate environment information for a network design, and a method for generating environment information may include determining a location difference value of a building between first information based on photographing and second information based on measurement, and correcting a location of an obstacle determined using the first information using the location difference value.

A method to generate upgrade recommendations for wireless wide area networks may include receiving parameters associated with an existing network configuration, customer usage, and a network demand, and identifying recommendations for locations of potential new cell sites corresponding with sectors within a geographical region associated with the network, where the identifying is based on the existing network configuration and customer usage. The method may include predicting a performance impact on the network based on the recommendations for potential new cell site locations, and selecting network upgrades based on the recommendations for potential new cell site locations, the network demand, and the predicted performance impact.

A device determines a throughput for each network slice of multiple network slices in a mobile network over a number of time windows, and predicts a future throughput for each network slice of the multiple network slices based on the determined throughput. The device determines an available bandwidth of a Radio Access Network (RAN) of the mobile network. The device allocates a respective portion of the available bandwidth of the RAN to each network slice of the multiple network slices based on the predicted future throughput for each network slice.

Methods and systems for reducing data sharing overhead based on similarities of datasets. In one aspect, there is a computer implemented method (1200) for reducing the amount of data transmitted to a network node (102) of a wireless communication system (100). The network node is configured to use the data to create or modify a model. The method is performed by the network node and comprises obtaining (s1202) information identifying a set of available datasets (152-158) and obtaining (s1204) a set of similarity scores. The method also comprises based on the obtained similarity scores, selecting (s1206) a subset of the set of available sets. The method further comprises transmitting (s1208) a request for each dataset included in the subset, receiving (s1210) the requested datasets, and using (s1212) the received datasets to construct or modify a model.

Methods and systems for reducing data sharing overhead based on similarities of datasets. In one aspect, there is a computer implemented method (1200) for reducing the amount of data transmitted to a network node (102) of a wireless communication system (100). The network node is configured to use the data to create or modify a model. The method is performed by the network node and comprises obtaining (s1202) information identifying a set of available datasets (152-158) and obtaining (s1204) a set of similarity scores. The method also comprises based on the obtained similarity scores, selecting (s1206) a subset of the set of available sets. The method further comprises transmitting (s1208) a request for each dataset included in the subset, receiving (s1210) the requested datasets, and using (s1212) the received datasets to construct or modify a model.

The present disclosure provides a big-data-mining-based wireless channel modeling method comprising: obtaining image information of a measurement environment and a channel impulse response data sample under a preset condition; obtaining at least one multipath wave and a channel parameter of each of the multipath wave according to the channel impulse response data sample using a channel parameter estimation algorithm; and clustering the at least one multipath wave according to the channel parameter of each of the multipath wave using a clustering algorithm to obtain at least one cluster; obtaining at least one scattering object in the measurement environment according to the image information of the measurement environment; matching each of the cluster with each of the scattering object to obtain a cluster kernel which is a cluster matching with the scattering object; establishing, a base wireless channel model under the preset condition according to all of the cluster kernel.