Abstract: Systems and methods may optimize power consumption for RAN devices. A device obtains field data of user equipment (UE) devices for a group of cells during a time interval. The field data includes signal strength measurements. The device computes cell boundaries for each cell based on the field data and predicts signal strength values inside each of the cell boundaries for a future time period. The device computes a cumulative overlap feature and a weighted signal strength feature for each cell of the group of cells during the future time period and generates an energy consumption predictive model that applies the cumulative overlap feature and the weighted signal strength feature for the future time period. The device determines, based on the energy consumption predictive model, optimal cell boundaries that correspond to a reduced energy consumption needed to meet service requirements during the future time period.

Abstract: A method, a device, and a non-transitory storage medium are described in which a TDD slot format selection service is provided. The service may calculate a distance between a radio access network device and an end device and use the distance to select a TDD configuration for the end device. The service may use other criteria to select the TDD configuration such as downlink or uplink ratios and/or interference value pertaining to a neighboring radio access network device. The service may also include scheduling of traffic adjustments based on a determined traffic bias associated with the end device and slot format symbols.

Abstract: A method, a device, and a non-transitory storage medium are described in which a test case generation service is provided. The service may include receiving and validating requirement text associated with a prospective test case and/or test script. The service may invoke a remedial procedure when the requirement text is not validated. The service may include selecting multiple types of classification models based on a model profile. The service may include aggregating probability values associated with classifications for sentences, and approving such classification that satisfy a threshold value. The service may further include generating test cases and/or test scripts. The service may generate a schedule for test cases and/or test scripts based on one or multiple criteria values generated by one or multiple predictive models.

Abstract: A system described herein may provide for the use of artificial intelligence/machine learning (“AI/ML”) techniques to generate models for various locations or regions (e.g., sectors) associated with one or more radio access networks (“RANs”) of a wireless network. The system may further use AI/ML techniques to generate interference models to reflect types and/or amounts of channel propagation metrics measured within the RAN. The system may further determine, based on attributes of a given sector, a sector model and/or a channel propagation model associated with the sector. Based on the sector model and/or the determined channel propagation model, one or more actions may be determined in order to enhance channel propagation metrics within the sector, such as at portions of the sector at which increased demand for wireless service is detected.

Abstract: Systems and methods may optimize power consumption for RAN devices. A device obtains field data of user equipment (UE) devices for a group of cells during a time interval. The field data includes signal strength measurements. The device computes cell boundaries for each cell based on the field data and predicts signal strength values inside each of the cell boundaries for a future time period. The device computes a cumulative overlap feature and a weighted signal strength feature for each cell of the group of cells during the future time period and generates an energy consumption predictive model that applies the cumulative overlap feature and the weighted signal strength feature for the future time period. The device determines, based on the energy consumption predictive model, optimal cell boundaries that correspond to a reduced energy consumption needed to meet service requirements during the future time period.

Abstract: A system described herein may identify a relative feature importance of a set of features in a modeling and/or simulation system. The same set of features may be provided to a group of different models. A relative feature importance of each feature of the set of features may be determined, on a per-model basis, based on comparing outputs of the model with and without particular features of the set of features. A relative feature of each feature may be further be determined on an inter-model basis by identifying features that are commonly ranked highly in the per-model rankings. An iterative process may evaluate the highest ranked, next-highest ranked, etc. features across multiple models. A simulation system may utilize the rankings to more efficiently perform one or more simulations, which may include omitting one or more features of the set of features when performing the simulations.

Abstract: In some implementations, a device may determine a signal to interference and noise ratio (SINR) value associated with a communication channel between a user equipment and the device. The device may select, based on the SINR value, a channel quality indicator (CQI) value associated with the communication channel or a sounding reference signal (SRS) from the UE to determine a multiple-input and multiple-output (MIMO) configuration for transmitting data to the UE. The device may determine the MIMO configuration according to the CQI value based on the SINR value being a first value. The device may determine the MIMO configuration according to the SRS based on the SINR value being a second value that is different than the first value. The device may transmit the data to the UE using the MIMO configuration.

Abstract: A system described herein may provide for the use of artificial intelligence/machine learning (“AI/ML”) techniques to generate models for various locations or regions (e.g., sectors) associated with one or more radio access networks (“RANs”) of a wireless network. The system may further use AI/ML techniques to generate interference models to reflect types and/or amounts of channel propagation metrics measured within the RAN. The system may further determine, based on attributes of a given sector, a sector model and/or a channel propagation model associated with the sector. Based on the sector model and/or the determined channel propagation model, one or more actions may be determined in order to enhance channel propagation metrics within the sector, such as at portions of the sector at which increased demand for wireless service is detected.

Abstract: A method, a device, and a non-transitory storage medium are described in which a TDD slot format selection service is provided. The service may calculate a distance between a radio access network device and an end device and use the distance to select a TDD configuration for the end device. The service may use other criteria to select the TDD configuration such as downlink or uplink ratios and/or interference value pertaining to a neighboring radio access network device. The service may also include scheduling of traffic adjustments based on a determined traffic bias associated with the end device and slot format symbols.

Abstract: A system described herein may provide for the use of artificial intelligence/machine learning (“AI/ML”) techniques to generate models for various locations or regions (e.g., sectors) associated with one or more radio access networks (“RANs”) of a wireless network. The system may further use AI/ML techniques to generate interference models to reflect types and/or amounts of channel propagation metrics measured within the RAN. The system may further determine, based on attributes of a given sector, a sector model and/or a channel propagation model associated with the sector. Based on the sector model and/or the determined channel propagation model, one or more actions may be determined in order to enhance channel propagation metrics within the sector, such as at portions of the sector at which increased demand for wireless service is detected.

Abstract: A system and method for load rebalancing are provided. A node B selects a subset of carriers from a plurality of carriers of the node B according to effective usages for the plurality of carriers, determines a heavily loaded carrier and a lightly loaded carrier from the subset of carriers according to effective usages of the subset of carriers, select a mobile device assigned to the heavily loaded carrier as a candidate for handoff, and performs a handoff of the selected mobile device from the heavily loaded carrier to the lightly loaded carrier.

Abstract: An apparatus, computer program, and method are provided for criteria-based conditional cell reselection. In operation, information is identified in connection with a mobile communication device being serviced by a first cell of a network utilizing a first frequency with a first priority. A determination is then made as to whether the information meets one or more criteria. Based on such determination, reselection is conditionally caused for the mobile communication device in connection with a second cell of the network utilizing a second frequency with a second priority.

Abstract: An apparatus, computer program, and method are provided for criteria-based conditional cell reselection. In operation, information is identified in connection with a mobile communication device being serviced by a first cell of a network utilizing a first frequency with a first priority. A determination is then made as to whether the information meets one or more criteria. Based on such determination, reselection is conditionally caused for the mobile communication device in connection with a second cell of the network utilizing a second frequency with a second priority.

Abstract: A system and method for load rebalancing are provided. A node B selects a subset of carriers from a plurality of carriers of the node B according to effective usages for the plurality of carriers, determines a heavily loaded carrier and a lightly loaded carrier from the subset of carriers according to effective usages of the subset of carriers, select a mobile device assigned to the heavily loaded carrier as a candidate for handoff, and performs a handoff of the selected mobile device from the heavily loaded carrier to the lightly loaded carrier.