Verizon Patent and Licensing Inc.

Google Inc.

Motorola Mobility LLC

Google Technology Holdings LLC

Krishna K Bellamkonda

Jin Yang

Nischal Patel

Vijay T Madhav

Ahmed Moussa

Brett L Robertson

Greg Augustus Rusu

Frank Jager

Istvan Janos Szini

Vijaya Paduvalli

Jing Ji

Hui Wang

Murali Narasimha

Xin Wang

Ratul Kumar Guha

Kristen Sydney Young

Eric LeRoy Krenz

Dean E Thorson

Ravi Potluri

John P Boos

Krishnamurthy Sreenath

Steve Kim

John Joseph Cooke

Malhar Vaishnav

Himanshu R Lokre

Yuanliang Lu

Pierre Muratory

Richard Hornby

Jie Song

Daniel J Schmidt

Arda Aksu

Maninder Singh Sehmbey

Morris B Bowers

Parvez Ahmad

Paul B Crosbie

Bryan S Nollett

Joseph F Cramer

Hideki Yoshino

Steven F Rice

Raghunath Hariharan

Arvind Kumar

Jaime Lugo

Laszlo Joseph Herczku

Jarrett K Simerson

Jitesh R Nair

Lalit K Mamtani

Christopher Janonis

Vijayalakshmi Paduvalli

Alexander Zerzghi

Daniel P Rose

Jlng Ji

Nischal Y Patel

Bryan S Nollitt

Maninder S Sehmbey

In some implementations, a system may obtain dynamic radio frequency (RF) response information associated with a user equipment and additional dynamic information associated with the user equipment. The system may determine, based on the dynamic RF response information and the additional dynamic information, a current behavior profile of the user equipment. The system may compare the current behavior profile and a baseline behavior profile of the user equipment to generate comparison information. The system may grant or deny, based on the comparison information, the user equipment access to a resource of a private network.

Systems and methods for authentication based on dynamic radio frequency response information

A system receives, from a user equipment, a request for access to a wireless communication network and identifies, based on the request, baseline radio frequency (RF) response information associated with the user equipment. The system obtains dynamic RF response information associated with the user equipment and authenticates, based on the dynamic RF response information and the baseline RF response information, the user equipment. The system thereby grants the user equipment access to the wireless communication network and determines context information associated with the user equipment. The system generates, based on the baseline RF response information and the context information, user equipment profile information and sends, to a base station associated with the wireless communication network, the user equipment profile information to facilitate access to the wireless communication network by the user equipment.

Systems and methods for providing access to a wireless communication network based on radio frequency response information and context information

A middle-tier self-organizing network (mSON) may obtain configuration parameters concerning a cluster, one or more operating profiles, and one or more usage schedules respectively associated with the one or more operating profiles, from a centralized self-organizing network (cSON). The mSON may identify a distributed self-organizing network (dSON) of one or more dSONs associated with the cluster and may select, based on identifying the dSON, an operating profile, of the one or more operating profiles, and a respective usage schedule of the one or more usage schedules. The mSON may generate, based on the configuration parameters, monitoring information, and may send the operating profile, the usage schedule, and the monitoring information to the dSON.

Systems and methods for a multi-tier self-organizing network architecture

A device may receive device data identifying Internet of Things (IoT) devices and may receive network data identifying network traffic patterns associated with the IoT devices. The device may process the device data and the network data, with a machine learning model, to determine parameters for charging each of the IoT devices and may determine that a particular IoT device of the IoT devices requires charging based on particular parameters associated with the particular IoT device. The device may receive location data identifying a location of the particular IoT device and may cause, based on the location data, one or more chargers to wirelessly provide a radio frequency power beam to the particular IoT device based on the particular parameters.

Systems and methods for wirelessly charging internet of things devices

Disclosed are systems and methods for providing a differentiated neighbor list that can be individually generated for a specific service (e.g., per service) based on network service information, which can include, but is not limited to, a user equipment (UE) group identifier (ID), network slicing and/or quality of service (QoS) flow, and the like. Neighbors within the differentiated list can be characterized based on, but not limited to, service types, distance to devices, transport costs, service locations, and the like, or some combination thereof. The disclosed framework can generate and dynamically update a differentiated neighbor list for specific types of services so that optimal neighbor selection is performed for the type of service a UE is operating within to ensure that a network connection is maintained at a threshold satisfying QoS.

System and method for generating and using a differentiated neighbor list

A method, a device, and a non-transitory storage medium are described in which a polymorphic algorithm service is provided. The service may coordinate and manage the execution of polymorphic algorithms of various optimization types in a multi-tier network, such as a radio access network or a self-organizing network. The service may coordinate the transition of execution of the polymorphic algorithms among the tiers of the multi-tier network, such that the polymorphic algorithm of a type may be active, for any given node, in a single tier of the multi-tier network. The service may monitor the yield of the optimizations based on various machine learning technologies, polices, and optimization targets. The polymorphic algorithms may operate in different time granularities in correspondence to the tiers of the multi-tier network.

Method and system for polymorphic algorithms interworking with a network

Systems and methods described herein provide for intelligent differentiated retransmissions in Radio Access Networks (RANs), such as Fifth Generation New Radio (5G-NR) networks, to provide consistent low latency for different service requirements. A network device determines if consistent low latency is required for a communication session requested by a user equipment (UE) device. The network device selects, based on the determining, retransmission settings for the communication session and sends the retransmission settings to an access station of a radio access network (RAN).

Systems and methods for intelligent differentiated retransmissions

A method, a device, and a non-transitory storage medium are described in which a radio service is provided. The radio service may provide radio resource control of radio resources of at least three different time scales. The radio service may include machine learning or artificial intelligent devices that include radio network information of the at least three different time scales.

Method and system for ran intelligent controller

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 determine Key Performance Indicators (&#x201c;KPIs&#x201d;) or other attributes that are of particular relevance or importance for a given sector model, and may determine actions to perform with respect to particular sectors in order to enhance performance according to the KPIs that are of particular relevance to a sector model determined with respect to the particular sectors.

Systems and methods for orchestration and optimization of wireless networks

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 radio frequency (&#x201c;RF&#x201d;) interference measured within the RAN. The system may further determine, based on received RF metrics for a given sector, a particular interference model associated with the sector. Based on a sector model associated with the sector and the determined interference model, one or more actions may be determined in order to remediate any potential interference associated with the sector or surrounding sectors.

Systems and methods for interference management in a radio access network

A system described herein may provide for the use of artificial intelligence/machine learning ('AI/ML') techniques to model energy usage information and energy saving techniques in various locations or regions (e.g., sectors) associated with one or more radio access networks ('RANs') of a wireless network. The system may determine energy saving techniques, such as radio frequency (&#x201c;RF&#x201d;) transmission modification in time and/or frequency domains, antenna mode modification, cell sleep mode, beamforming parameters, and/or other suitable techniques to modify energy consumption at such sectors. The system may balance energy savings with maintaining Quality of Service (&#x201c;QoS&#x201d;) metrics at an acceptable level.

Systems and methods for performance-aware energy saving in a radio access network

A computer device may include a memory configured to store instructions and a processor configured to execute the instructions to classify a plurality of base station sectors into a plurality of context categories based on a context that includes a measure of cell density and a measure of mobility. The processor may be further configured to select a context category from the plurality of context categories; determine a coverage and quality optimization parameter adjustment, a capacity optimization parameter adjustment, and a accessibility optimization parameter adjustment for a subset of the base station sectors classified in the selected context category; and instruct base stations associated with the subset of base station sectors to implement the coverage and quality optimization parameter adjustment, the capacity optimization parameter adjustment, and the accessibility optimization parameter adjustment.

Contextualized network optimization

Systems and methods model earth stations and other captive terrestrial sites as simulated cell sites in a radio access network (RAN) to identify potential cellular network interferers with the earth stations. A computing device selects an earth station within a geographic area of a RAN segment and model the earth station as a cell within the RAN segment, wherein the modeling creates a simulated earth station cell. The computing device obtains sector carrier data for cells in the RAN segment and scores, based on the sector carrier data, neighboring cells to the simulated earth station cell. The scoring indicates a level of potential interference of the neighboring cells with the earth station based on geo-spatial relevance. The computing device identifies projected mobility interference in neighboring cells to the earth station and provides prioritization recommendations for interference mitigation for the earth station based on the scoring and the identifying.

Systems and methods for mitigating cellular and terrestrial captive site interference

A system described herein may provide for the use of modeling techniques to generate models associated with various locations or regions (e.g., sectors) associated with one or more radio access networks ('RANs') of a wireless network, as well as for one or more mobile radio units ('MRUs') that include wireless network infrastructure and which may be dynamically moved from one location to another. The system may identify attributes of a location within the network at which the MRU is located, and may automatically configure base stations of the RAN as well as the wireless network infrastructure of the MRU in order to optimize the operation of the RAN, as well as to gain optimal usage of the MRU.

Systems and methods for dynamic wireless network configuration based on mobile radio unit location

Growing community of inventors

Roanoke, TX, United States of America

Krishna K Bellamkonda