Abstract: A computer-implemented method generates an estimate of a real-time distribution density of wireless devices in a geographic area. The method includes receiving real-time network activity data collected at cell sites. The method includes processing the real-time network activity data with an ML model. The ML model is trained based on non-real-time network activity data indicating network activities as well as location data of wireless devices served by the cell sites. The ML model is configured to predict a real-time distribution of wireless devices in the geographic area at different points in time. The method includes predicting a real-time distribution of the wireless devices based on a pattern of network activities and locations of wireless devices. The method also includes generating an estimate of a distribution density of the wireless devices in the geographic area based on the predicted real-time distribution and adjusted by the real-time count of network activities.

Abstract: Techniques directed to utilizing networked devices to enhance user experience are described. In an example, sensor data received from sensor(s) associated with a facility can be used to determine a presence of a user equipment (UE) within or proximate to the facility. Based at least in part on the sensor data, an account associated with the UE can be accessed. Based at least in part on account data associated with the account, a distribution of network technology can be modified to temporarily enable the UE to access a service that is not otherwise available to at least one other UE within or proximate to the facility.

Abstract: User equipment (UE) can include a spectrum analyzer to monitor characteristics of transmission channels. The user equipment can monitor a 600 MHz spectrum and associated channels, for example, to determine if the spectrum is free of interference or is currently occupied. The UE can analyze a received signal strength indication (RSSI), a reference signal received power (RSRP), a reference signal received quality (RSRQ), and signal-to-interference-plus-noise ratio (SINR), for example, to distinguish between types of interference if a channel is occupied. User equipment (UE) can aggregate data and report such data to a network device further aggregate the data and to generate reports. Network components can be deployed or optimized based at least in part on network metrics provided by individual UEs or aggregated data provided by a plurality of UEs. In some instances, the UE can be a mobile phone of a customer to gather metrics in a distributed manner.

Abstract: A base station and computing resources to be used by a user equipment (UE) within a network can be selected based on information, such as an identification of base station(s) that may be used by the UE, and the location and performance of the base station(s) and the available computing resources. In some examples, when a mobility controller determines to switch the base station for a UE, the computing resources used to perform application processing can also be switched. For instance, when the current base station is switched, the computing resources used to perform application processing for the UE may be moved to computing resources that are closer to the different base station. In other examples, the mobility controller may determine to delay switching the UE to the different base station if the performance of the mobile application would be better using the current base station and current computing resources.

Abstract: A computing device of a telecommunication network can receive crowd-sourced Wi-Fi reports submitted by user equipment based on scans for beacon signals broadcast by Wi-Fi access points. From the crowd-sourced Wi-Fi reports, the computing device can determine when Licensed Assisted Access (LAA) transmissions over channels of an unlicensed spectrum are safe and are not expected to interfere with Wi-Fi transmissions.

Abstract: Solutions for accelerating cell search and selection by a user equipment (UE) include: detecting, by the UE, a network exit; determining, by the UE, a network reentry condition; based on at least mobility data for the UE and a network connectivity context history, determining, by the UE, using a machine learning (ML) component, a set of priority reentry cells; attempting network reentry with the set of priority reentry cells; and based on at least failing network reentry with the set of priority reentry cells, attempting network reentry with a cell search. In some examples, mobility data for the UE is also used for determining the set of priority reentry cells. By searching the set of priority reentry cells first , rather than starting with a blind search, the UE may save not only battery power, but also reconnect to the network more quickly, thereby improving the user experience.

Abstract: Solutions for focused cellular network paging of a user equipment (UE) include: detecting, by a network, a network exit of the UE from a last known cell and an exit condition for the UE; determining, by the network, a network paging condition; based on at least the last known cell and the exit condition for the UE, determining, by the network, a set of priority paging cells; paging the UE with the set of priority paging cells; and based on at least failing to receive a response by paging the UE with the set of priority paging cells, paging the UE with a larger set of cells. In some examples, the network topology is also used for determining the set of priority paging cells. By starting with the set of priority paging cells, rather than wider global paging, fewer cells are used for paging, thereby saving network bandwidth.

Abstract: A base station and computing resources to be used by a user equipment (UE) within a network can be selected based on information, such as an identification of base station(s) that may be used by the UE, and the location and performance of the base station(s) and the available computing resources. In some examples, when a mobility controller determines to switch the base station for a UE, the computing resources used to perform application processing can also be switched. For instance, when the current base station is switched, the computing resources used to perform application processing for the UE may be moved to computing resources that are closer to the different base station. In other examples, the mobility controller may determine to delay switching the UE to the different base station if the performance of the mobile application would be better using the current base station and current computing resources.

Abstract: In some examples, a terminal can establish wireless communication with a base station. The terminal can determine a challenge, transmit the challenge, receive a response, and determine that the response is valid. The terminal can, in response, establish a secure network tunnel to a network node. In some examples, a terminal can determine a first communication parameter associated with communication with the base station. The terminal can receive data indicating a second communication parameter via a secure network tunnel. The terminal can determine that the communication parameters do not match, and, in response, provide an indication that an attack is under way against the network terminal. Some example terminals transmit a challenge, determine a response status associated with the challenge, and determine that an attack is under way based on the response status.

Abstract: Techniques directed to utilizing networked devices to enhance user experience are described. In an example, sensor data received from sensor(s) associated with a facility can be used to determine a presence of a user equipment (UE) within or proximate to the facility. Based at least in part on the sensor data, an account associated with the UE can be accessed. Based at least in part on account data associated with the account, a distribution of network technology can be modified to temporarily enable the UE to access a service that is not otherwise available to at least one other UE within or proximate to the facility.

Abstract: When deploying or upgrading an enhanced cellular communication system, a site survey may be performed by configuring cellular devices in a given area to report various operational metrics. The devices may also be configured to report environmental data that can be used to determine whether the devices are indoors or outdoors, which may be useful when interpreting the operational metrics. The environmental signatures may include an audio signature, which may comprise an audio impulse response produced by an acoustic echo canceller (AEC) of the device. The environmental signatures may further include a light signature, which may be based on frequency components of ambient light. The environmental signatures may further include an audio signature, which may be based on characteristics of radio signals received by the device. Machine learning techniques may be used, with the environmental signatures as features, to predict whether a given device is indoors or outdoors.

Abstract: A computing device of a telecommunication network can receive crowd-sourced Wi-Fi reports submitted by user equipment based on scans for beacon signals broadcast by Wi-Fi access points. From the crowd-sourced Wi-Fi reports, the computing device can determine when Licensed Assisted Access (LAA) transmissions over channels of an unlicensed spectrum are safe and are not expected to interfere with Wi-Fi transmissions.

Abstract: In some examples, a terminal can establish wireless communication with a base station. The terminal can determine a challenge, transmit the challenge, receive a response, and determine that the response is valid. The terminal can, in response, establish a secure network tunnel to a network node. In some examples, a terminal can determine a first communication parameter associated with communication with the base station. The terminal can receive data indicating a second communication parameter via a secure network tunnel. The terminal can determine that the communication parameters do not match, and, in response, provide an indication that an attack is under way against the network terminal. Some example terminals transmit a challenge, determine a response status associated with the challenge, and determine that an attack is under way based on the response status.

Abstract: Techniques directed to utilizing networked devices to enhance user experience are described. In an example, sensor data received from sensor(s) associated with a facility can be used to determine a presence of a user equipment (UE) within or proximate to the facility. Based at least in part on the sensor data, an account associated with the UE can be accessed. Based at least in part on account data associated with the account, a distribution of network technology can be modified to temporarily enable the UE to access a service that is not otherwise available to at least one other UE within or proximate to the facility.

Abstract: User equipment (UE) can include a spectrum analyzer to monitor characteristics of transmission channels. The user equipment can monitor a 600 MHz spectrum and associated channels, for example, to determine if the spectrum is free of interference or is currently occupied. The UE can analyze a received signal strength indication (RSSI), a reference signal received power (RSRP), a reference signal received quality (RSRQ), and signal-to-interference-plus-noise ratio (SINR), for example, to distinguish between types of interference if a channel is occupied. User equipment (UE) can aggregate data and report such data to a network device further aggregate the data and to generate reports. Network components can be deployed or optimized based at least in part on network metrics provided by individual UEs or aggregated data provided by a plurality of UEs. In some instances, the UE can be a mobile phone of a customer to gather metrics in a distributed manner.

Abstract: A computing device of a telecommunication network can receive crowd-sourced Wi-Fi reports submitted by user equipment based on scans for beacon signals broadcast by Wi-Fi access points. From the crowd-sourced Wi-Fi reports, the computing device can determine when Licensed Assisted Access (LAA) transmissions over channels of an unlicensed spectrum are safe and are not expected to interfere with Wi-Fi transmissions.

Abstract: User equipment (UE) can include a spectrum analyzer to monitor characteristics of transmission channels. The user equipment can monitor a 600 MHz spectrum and associated channels, for example, to determine if the spectrum is free of interference or is currently occupied. The UE can analyze a received signal strength indication (RSSI), a reference signal received power (RSRP), a reference signal received quality (RSRQ), and signal-to-interference-plus-noise ratio (SINR), for example, to distinguish between types of interference if a channel is occupied. User equipment (UE) can aggregate data and report such data to a network device further aggregate the data and to generate reports. Network components can be deployed or optimized based at least in part on network metrics provided by individual UEs or aggregated data provided by a plurality of UEs. In some instances, the UE can be a mobile phone of a customer to gather metrics in a distributed manner.

Abstract: This disclosure describes techniques for capturing radio frequency (RF) signal over the air, processing spectrum power data, and uploading data to a central data repository for analysis and reporting. Captured RF signals are filtered and amplified via a front-end module of a probe that includes a filter and a low noise amplifier (LNA). Output signals can be decoded into digital TV metadata and image captures via a TV tuner and spectrum power, and then analyzed via a software defined radio (SDR). Digital TV metadata and images, as well as SDR data, can be uploaded and collected at a server. The server processes collected spectrum data from SDR into spectrum reports. The server can also analyze collected digital TV metadata to identify with increased accuracy RF sources originating from transmitting TV facilities.

Abstract: In some examples, a terminal can establish wireless communication with a base station. The terminal can determine a challenge, transmit the challenge, receive a response, and determine that the response is valid. The terminal can, in response, establish a secure network tunnel to a network node. In some examples, a terminal can determine a first communication parameter associated with communication with the base station. The terminal can receive data indicating a second communication parameter via a secure network tunnel. The terminal can determine that the communication parameters do not match, and, in response, provide an indication that an attack is under way against the network terminal. Some example terminals transmit a challenge, determine a response status associated with the challenge, and determine that an attack is under way based on the response status.

Abstract: Techniques directed to utilizing networked devices to enhance user experience are described. In an example, sensor data received from sensor(s) associated with a facility can be used to determine a presence of a user equipment (UE) within or proximate to the facility. Based at least in part on the sensor data, an account associated with the UE can be accessed. Based at least in part on account data associated with the account, a distribution of network technology can be modified to temporarily enable the UE to access a service that is not otherwise available to at least one other UE within or proximate to the facility.