Disclosed are methods, systems, apparatus, and computer programs for self-learning geofences is disclosed. In one aspect, a method involves gathering a plurality of data points associated with one or more Citizens Broadband Radio Service (CBRS) deployers; determining respective identifiers of the one or more CBRS deployers associated with the plurality of data points; clustering, based on the respective identifiers of the one or more CBRS deployers, the plurality of data points into one or more clusters, where each cluster is associated with one of the one or more CBRS deployers, and where each cluster is associated with a geofence of a network of the one or more CBRS deployers; identifying an opportunity for uploading the one or more clusters to a central server; and uploading the one or more clusters to the central server during the identified opportunity.

Disclosed are methods, systems, apparatus, and computer programs for self-learning geofences is disclosed. In one aspect, a method involves gathering a plurality of data points associated with one or more Citizens Broadband Radio Service (CBRS) deployers; determining respective identifiers of the one or more CBRS deployers associated with the plurality of data points; clustering, based on the respective identifiers of the one or more CBRS deployers, the plurality of data points into one or more clusters, where each cluster is associated with one of the one or more CBRS deployers, and where each cluster is associated with a geofence of a network of the one or more CBRS deployers; identifying an opportunity for uploading the one or more clusters to a central server; and uploading the one or more clusters to the central server during the identified opportunity.

Described herein are techniques, devices, and systems for providing an optimal voice experience over varying radio frequency (RF) conditions while using EVS audio codecs. A user equipment (UE) may adaptively transition between using a music-capable EVS codec (e.g., EVS-FB) as a default audio codec that provides a first audio bandwidth and a different EVS audio codec that provides a second audio bandwidth that is less than the first audio bandwidth. The transition to the different EVS audio codec may occur in response to determining a value indicative of a RF condition associated with a serving base station is less than a threshold value, which allows for providing preserving at least a minimal level of voice quality in degraded RF conditions.

Described herein are techniques, devices, and systems for providing an optimal voice experience over varying radio frequency (RF) conditions while using EVS audio codecs. A user equipment (UE) may adaptively transition between using a music-capable EVS codec (e.g., EVS-FB) as a default audio codec that provides a first audio bandwidth and a different EVS audio codec that provides a second audio bandwidth that is less than the first audio bandwidth. The transition to the different EVS audio codec may occur in response to determining a value indicative of a RF condition associated with a serving base station is less than a threshold value, which allows for providing preserving at least a minimal level of voice quality in degraded RF conditions.

A machine learning method performed by a communication network monitoring device in which an incoming signaling record is received that includes radio signal attributes from a UE in the cellular communication network. A determination is made as to whether the UE incoming signaling record contains location (GPS) data. If the UE incoming signaling record contains GPS data, a machine learning model is generated for determining a location of future UEs in the communication network utilizing the GPS data and the radio signal attributes from the incoming UE signaling record. And if GPS data is not included in the UE incoming signaling record, then the geolocation for the UE is predicted using machine learning techniques utilizing a previous generated machine learning model as applied to the radio signal attributes from the incoming UE signaling record.

A machine learning method performed by a communication network monitoring device in which an incoming signaling record is received that includes radio signal attributes from a UE in the cellular communication network. A determination is made as to whether the UE incoming signaling record contains location (GPS) data. If the UE incoming signaling record contains GPS data, a machine learning model is generated for determining a location of future UEs in the communication network utilizing the GPS data and the radio signal attributes from the incoming UE signaling record. And if GPS data is not included in the UE incoming signaling record, then the geolocation for the UE is predicted using machine learning techniques utilizing a previous generated machine learning model as applied to the radio signal attributes from the incoming UE signaling record.

A system that incorporates the subject disclosure may perform, for example, a method for receiving interference information, identifying a plurality of interferers, approximating a location of the plurality of interferers, and adjusting an antenna pattern of an antenna. The method can include determining traffic loads and adjusting the antenna pattern according to the traffic loads. Other embodiments are disclosed.

A system that incorporates the subject disclosure may perform, for example, a method for receiving interference information, identifying a plurality of interferers, approximating a location of the plurality of interferers, and adjusting an antenna pattern of an antenna. The method can include determining traffic loads and adjusting the antenna pattern according to the traffic loads. Other embodiments are disclosed.

A system includes a method for detecting a signal interference in a communication signal of a wireless communication system. An identified source of the signal interference is determined according to an interference profile of a plurality of interference profiles associated with an interference profile library having information that approximates characteristics of the signal interference. The signal interference of the communication signal is mitigated according to an interference parameter associated with the identified source by filtering the communication signal according to the interference parameter.

A system includes a method for detecting a signal interference in a communication signal of a wireless communication system. An identified source of the signal interference is determined according to an interference profile of a plurality of interference profiles associated with an interference profile library having information that approximates characteristics of the signal interference. The signal interference of the communication signal is mitigated according to an interference parameter associated with the identified source by filtering the communication signal according to the interference parameter.