A method for identifying a wireless signal transmission characteristic in a wireless communication system according to one embodiment of the present specification includes the steps for: identifying a signal transmission location; identifying a structure; identifying at least one radio wave incoming structure located on the structure, and identifying a transmission characteristic of a wireless signal transmitted from the signal transmission location on the basis of information on the at least one radio wave incoming structure.

A wave generation module can illustrate an aggregate wave pattern in a virtual environment representative of a physical environment in 3-D. The aggregate wave pattern takes into account an effect of the composition characteristics of the structural walls and fixtures on the wave generating characteristics for the of access points.

Systems and methods to identify whether user traffic is generated indoors (e.g., from within a building) or outdoors for a variety of applications, including improving capacity planning, identifying new products offerings, troubleshooting/planning, competitive analysis, planning optimum locations of capacity planning solution deployment, traffic offload analysis, etc. are disclosed. The method receives and aggregates data from a variety of sources, including customer geolocation data, network data, street/building maps, indoor/outdoor classification of traffic, etc. to generate demand density maps that depict network traffic usage patterns at a building level. The method can then use the demand density maps to identify hotspots, evaluate in-building coverage, and select and rank optimum solutions and/or locations for capacity improvement solutions deployment. As a result, a telecommunications service provider is able to efficiently and economically identify targeted solutions and locations to expand capacity of cell sites and improve customer experiences.

Provided are a deep learning-based beamforming communication system and method, wherein in an indoor environment using millimeter wave communication, in response to reference signals transmitted from a base station to at least one user terminal, reference signal received power and location information for each user terminal location are received from each user terminal and a fingerprint DB is constructed, and from the constructed fingerprint data, a user model is constructed on the basis of reference signal received power for each user terminal location and a blockage model is constructed on the basis of reference signal received power according to each blockage located between the base station and the user terminal. Location information and data traffic are received from the at least one user terminal, a beam index of the user terminal corresponding to the received data traffic is derived from a deep neural network, and a communication channel between the base station and a user is formed with the derived beam index, whereby reliability and a data transfer rate are improved in an indoor communication environment.

A service qualification platform may receive, from a user device, a service request to qualify a unit of a multi-unit building to receive a service. The service qualification platform may obtain a service coverage mapping associated with the multi-unit building and may provide, to the user device and via a user interface that is associated with a geographic information system, a unit location request for an indication of a location of the unit within the multi-unit building. The service qualification platform may receive unit location information that is associated with the indication and may determine a service qualification metric based on the service coverage mapping and the unit location information, wherein the service qualification metric is associated with a capability of receiving the service within the unit. The service qualification platform may perform an action associated with the service qualification metric.

Techniques are disclosed relating to identifying connection quality for locations within an environment. A first computing device may collect environment information pertaining to a layout of an environment in which the first computing device and a second computing device are located. The first computing device may be in wireless communication with the second computing device. The first computing device may determine, based on the environment information, a location within the environment to locate the first computing device to improve the wireless communication. The first computing device may provide an indication of the location to a user of the first computing device.

Embodiments herein disclose methods for handling a data driven model in a wireless communication network. The method includes identifying, by a first electronic device, a common data driven model capability between a capability information of one or more first data driven model and a capability information of one or more second data driven model. The one or more first data driven model is associated with the first electronic device and the one or more second data driven model is associated with the second electronic device. Further, the method includes performing, by the first electronic device, one of: storing the common data driven model capability in the first electronic device on identifying the common data driven model capability, and disabling a data driven model capability in the first electronic device on not identifying the common data driven model capability.

A method for identifying a client device in a network, and a first radio in the network that is coupled with the client device is provided. The method includes determining one or more sequences of roaming events for multiple client devices in the network, evaluating a performance metric for a roaming event and evaluating an interaction between the client device and one or more radios involved in the roaming events for the plurality of client devices. The method also includes selecting a second radio in the network based at least in part on (1) the one or more sequences of roaming events, (2) the performance metric, and (3) the interaction between the client device and the one or more radios, and recommending switching the client device from the first radio to the second radio. A system and a predictive tool to perform the above method are also provided.

An automatic wireless fine-grained ratio map construction and adaptation system may include a Gaussian process regression (GPR) model constructed with real wireless received signal strength (RSS) measurements collected in a free space to provide coarse RSS estimation in a constrained space, and a generative adversarial network (GAN) to provide fine-grained RSS estimation in the constrained space by using an output of GPR as an input for a generator of GAN, modeling the irregular RSS distributions in complex indoor environments. The system may generate realistic RSS data in the constrained space that has not been manually site-surveyed.

The techniques describe herein involve receiving, from a user device, captured network data associated with an altitude and analyzing the network data at a network node. The network node can determine a coverage index associated with the altitude and compare the coverage index with an aggregated coverage index to determine a network response. The network response can include deploying and/or modifying a network component associated with the altitude. Additionally, the network node can update a multidimensional coverage map based on the coverage index and/or the network response.