Systems and methods for localizing individuals in a region using wireless signals in accordance with embodiments are illustrated. One embodiment includes a method for localizing individuals in a region between wireless devices of a system. The method receives wireless signal strength data for signals transmitted along signal paths between several wireless playback devices transmitting on a wireless channel during synchronous playback of media content by the several wireless playback devices and determines a first signal strength for each of several portions of the wireless channel. The method calculates, for each signal path between each of the several wireless playback devices, a difference in the determined first signal strength from a second signal strength for each of the several subcarriers, and determines, based on the calculated differences, a state for a set of one or more individuals in the region.

A method for identifying radio signal transmission characteristics in a wireless communication system and an apparatus therefor are provided. The method may include identifying a signal transmission site, identifying a signal reception site, finding an area where a tree is present between the signal transmission site and the signal reception site, checking characteristics of the crown of the tree and characteristics of the trunk of the tree, and examining transmission characteristics of a radio signal sent from the signal transmission site to the signal reception site on the basis of the characteristics of the crown and the trunk. The method and apparatus relate to a communication method and system for converging a 5th-Generation (5G) communication system for supporting higher data rates beyond a 4th-Generation (4G) system with a technology for internet of things (IoT), and may be applied to intelligent services based on the 5G communication and the IoT-related technologies.

An electronic apparatus and a control method are provided. The electronic apparatus includes a transceiver, a memory configured to store an artificial intelligence (AI) model, and a processor configured to control the transceiver to receive environment information from at least one of a plurality of devices that are connected to the electronic apparatus, determine that a predicted device of the plurality of devices will lose a network connection based on the first AI model and the environment information, and in response to determining the predicted device will lose the network connection, maintain the network connection of the predicted device through another device of the plurality of devices. The electronic apparatus may use a rule-based model or an AI model trained by using at least one of a machine learning algorithm, a neural network algorithm, or a deep-learning algorithm.

A method is provided. The method includes receiving a first dimension set, extracting a first latent feature set from the first dimension set, training a first base predictor based on the first feature set, generating a second dimension set based on the first dimension set, the second dimension set having fewer dimensions than the first dimension set, extracting a second latent feature set from the second dimension set, training a second base predictor based on the second feature set, and generating a traffic prediction based on the first base predictor and the second base predictor.

A method is provided. The method includes receiving a first dimension set, extracting a first latent feature set from the first dimension set, training a first base predictor based on the first feature set, generating a second dimension set based on the first dimension set, the second dimension set having fewer dimensions than the first dimension set, extracting a second latent feature set from the second dimension set, training a second base predictor based on the second feature set, and generating a traffic prediction based on the first base predictor and the second base predictor.

The invention relates to a UAV arrangement device based on machine learning for maximizing communication throughput, the UAV arrangement device including: a communication module unit that receives location information and communication demand information of a user and transmits UAV arrangement information; a user information unit that stores and analyzes the location information and the communication demand information of the user; a UAV information unit that stores basic UAV information including UAV communication throughput; and a clustering algorithm modeling unit that calculates a location at which communication throughput is maximized, with a clustering algorithm based on the location information and the communication demand information of the user. The invention relates to a UAV arrangement method based on machine learning for maximizing communication throughput, the UAV arrangement method including: checking location information of a user; checking communication demand of a user; calculating central nodes of respective clusters at which communication throughput is maximized, by using a clustering algorithm; and arranging unmanned aerial vehicles of central nodes of the respective clusters.

A method for determining a layout of a wireless communication network is provided in the present invention. Numerous realizations of user device placement in a considered geometry are measured to reflect the practical distribution of the user devices in a more accurate way than the conventional approach which emulates the randomness of placements of user devices using a tractable stochastic process. Moreover, a scenario sampling approach is used to provide a lower-complexity and higher efficient way to yield optimal base station deployment results while guaranteeing the quality of service of a specified majority of the overall user device realizations.

A method for enhancing uplink and downlink coverage is applied in a base station for the benefit of a terminal user. The base station receives from the user terminal an uplink scheduling request and GPS location. In response, the base station determines a penetration loss level of the GPS location, the penetration loss being attenuation of signals when signals penetrate an outer structure of a building. The base station further determines a transmission frequency of the uplink according to the penetration loss level which is set by reference to base station Tables of penetration loss for buildings and areas within its coverage area, and generates a scheduling strategy comprising the transmission frequency of the uplink, sending the scheduling strategy including the transmission frequency of the uplink to the user terminal for use in intercommunication.

A method for enhancing uplink and downlink coverage is applied in a base station for the benefit of a terminal user. The base station receives from the user terminal an uplink scheduling request and GPS location. In response, the base station determines a penetration loss level of the GPS location, the penetration loss being attenuation of signals when signals penetrate an outer structure of a building. The base station further determines a transmission frequency of the uplink according to the penetration loss level which is set by reference to base station Tables of penetration loss for buildings and areas within its coverage area, and generates a scheduling strategy comprising the transmission frequency of the uplink, sending the scheduling strategy including the transmission frequency of the uplink to the user terminal for use in intercommunication.

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.