Verizon Patent and Licensing Inc.

Future Wei Technologies, Inc.

Lucent Technologies Inc.

Huawei Technologies Co., Limited

Mark Ernest Newbury

Farid Khafizov

Sachin R Vargantwar

Chin Chiu

Jack Anthony Smith

Venkata Sambasivarao Dhulipalla

SaiRamesh Nammi

Zinan Lin

Suman Das

Limei Wang

Hong Yang

Julia Wang

Hai Zhou

Iyad Alfalujah

Matthijs Andries Visser

Utku Azman

Kamalaharan Dushyanthan

Alpaslan Gence Savas

Monica Casado-Fernandez

Keith Butterworth

Matthijs A Visser

Lyad Alfalujah

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 device computes cell boundaries for each cell and computes a cumulative overlap feature within the geographic area of interest for each cell of the group of cells during a future time period. The device determines updated cell boundaries that correspond to a reduced energy consumption needed to meet service requirements during the future time period.

Method and apparatus for optimizing RAN power consumption

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.

Systems and methods for determining a massive multiple-input and multiple-output configuration for transmitting data

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.

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.

TDD slot format selection service

The disclosed embodiments are directed toward improvements in dynamic spectrum sharing (DSS) between cellular network technologies. In one embodiment, a method is disclosed comprising initiating scheduling for a resource in a slot and determining a neighboring base station associated with user equipment (UE). The method then determines a type of interference associated with the neighboring base station and the slot and identifies an outer loop link adaptation (OLLA) value associated with the type of interference. An effective data rate is calculated based on the OLLA value and the method completes scheduling using the effective data rate.

Interference avoidance based on multiple outer loop link adaptation (OLLA) processes

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.

Systems and methods for modification of radio access network parameters based on channel propagation models generated using machine learning techniques

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.

System and method for load rebalancing

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.

Apparatus, computer program, and method for criteria-based conditional cell reselection

Embodiments are provided for calibration, preprocessing, and segmentation for user localization and network traffic density estimation. The embodiments include sending, from a network component, a request to a plurality of user equipment (UEs) to participate in reporting localization data. Reports for localization data including no-lock reports are received from at least some of the UEs. The no-lock reports indicate indoor UEs among the UEs. The network preprocesses the localization data by eliminating, from the localization data, data that increases the total noise to signal ratio. The localization data is then processed using a model that distinguishes between different buildings. This includes associating, according to a radio map, radio characteristics in the localization data with corresponding bins in a non-uniform grid of coverage. The non-uniform grid is predetermined to maximize uniqueness between the radio characteristics. The indoor UEs are associated with corresponding buildings using the no-lock reports data.

System and method for localization and traffic density estimation via segmentation and calibration sampling

The per sector or per cell downlink capacity in a spread spectrum wireless network is determined using a single test mobile that is operating within a background of simulated co-channel interference being broadcast in each sector by all cell sites in the network whose capacity is of interest. From all sectors, an Orthogonal Channel Noise Simulator (OCNS) generates a multiple number of calibrated test signals, which are aggregated and broadcast downlink, where each transmitted test signal simulates the downlink power transmitted to a live mobile. Each component test signal in the aggregated OCNS output transmitted in each sector is a sample of a stochastic process with a set mean and standard deviation that are determined from the mean and standard deviation of the power of a real signal transmitted downlink to the test mobile as it is moved around the network's coverage area. The capacity of the sector is determined by increasing the number of test signals in each aggregated OCNS output until measurable performance is compromised.

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Hillsborough, NJ, United States of America

Mark Ernest Newbury