# US 11,451,997 B2 · Systems and methods for a self-organizing network based on user equipment information > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,451,997 B2 - Document ID: US11451997B2 - Status: Granted - Kind: B2 - Application number: 16/887,873 - Filed: 2020-05-29 - Published: 2020-09-17 - Granted: 2022-09-20 - Earliest claimed: 2018-01-02 - Assignees: Verizon Patent and Licensing Inc. - Applicants: Verizon Patent and Licensing Inc. - Inventors: Jin Yang; Ratul K. Guha; Edmond K. Chan - Web page: https://patentgrep.com/patent/US11451997B2 ## CPC - H04L 43/16: Threshold monitoring - H04W 24/02: Arrangements for optimising operational condition - H04W 28/021: {in wireless networks with changing topologies, e.g. ad-hoc networks (self-organizing networks H04W84/18)} - H04W 36/13: {Cell handover without a predetermined boundary, e.g. virtual cells} - H04L 41/16: using machine learning or artificial intelligence - H04L 67/12: specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks - H04W 16/28: using beam steering - H04W 28/0215: {based on user or device properties, e.g. MTC-capable devices (services for machine-to-machine communication [M2M] or machine type communication [MTC] H04W4/70; wireless resource selection or allocation plan definition based on terminal or device properties H04W72/51)} - H04W 40/34: Modification of an existing route - H04W 84/18: Self-organising networks, e.g. ad-hoc networks or sensor networks - Y04S 40/00: Systems for electrical power generation, transmission, distribution or end-user application management characterised by the use of communication or information technologies, or communication or information technology specific aspects supporting them - H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M) - H04W: WIRELESS COMMUNICATION NETWORKS (broadcast communication H04H; communication systems using wireless links for non-selective communication, e.g. wireless extensions H04M1/72) - Y04S: SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS ## Abstract A computer device may include a memory configured to store instructions and a processor configured to execute the instructions to determine network slices for a plurality of user equipment (UE) devices associated with a base station wherein a particular network slice identifies a particular service category or a UE device attribute category associated with particular ones of the plurality of UE devices, determine a distribution of network slices for the base station based on the determined network slices for the plurality of UE devices, and determine an adjustment for an optimization parameter based on the distribution of network slices for the base station. The computer device may be further configured to adjust the optimization parameter for the base station based on the determined adjustment, wherein adjusted optimization parameter is used by the base station to manage data traffic associated with the plurality of UE devices. ## Claims ### Claim 1 A method comprising: determining, by a device, weights for different ones of a plurality of network slices associated with a base station, based on a ratio of a number of user equipment (UE) devices associated with a particular network slice to a total number of UE devices serviced by the base station; computing, by the device, network slice adjustments for an optimization parameter for different ones of the plurality of network slices; computing, by the device, an adjustment for the optimization parameter based on the computed network slice adjustments and the determined weights; and adjusting, by the device, the optimization parameter for the base station based on the computed adjustment, wherein the adjusted optimization parameter is used by the base station to manage data traffic for the UE devices serviced by the base station. ### Claim 2 (depends on 1) The method of claim 1, further comprising: receiving an indication of importance for the particular network slice of the plurality of network slices; and adjusting a weight associated with the particular network slice, in response to receiving the indication of importance for the particular network slice. ### Claim 3 (depends on 1) The method of claim 1, further comprising: determining a performance score for a wireless access network associated with the base station based on the adjusted optimization parameter; and reverting the optimization parameter to a previous value, when the determined performance score does not result in an improvement over a previous performance score. ### Claim 4 (depends on 1) The method of claim 1, further comprising: determining positioning and route profiles for the UE devices serviced by the base station, wherein the positioning and route profiles indicate historical position and routes associated with particular ones of the UE devices serviced by the base station; and wherein adjusting the optimization parameter for the base station is further based on the determined positioning and route profiles for the UE devices serviced by the base station. ### Claim 5 (depends on 1) The method of claim 1, wherein the optimization parameter includes at least one of: a coverage optimization parameter; an antenna tilt parameter; a power distribution parameter; a retransmission parameter; a handover parameter; a neighbor list changes parameter; a load balancing parameter; a delay optimization parameter; a carrier optimization parameter; or a random access channel parameter. ### Claim 6 (depends on 1) The method of claim 1, wherein adjusting the optimization parameter for the base station is further based on one or more performance indicators, wherein the one or more performance indicators include at least one of: an accessibility performance indicator, a retainability performance indicator, a mobility performance indicator, a service integrity performance indicator, a utilization performance indicator, an availability performance indicator, a traffic performance indicator, or a response time performance indicator. ### Claim 7 (depends on 1) The method of claim 1, further comprising: determining the plurality of network slices associated with the base station using a machine learning classifier trained to determine network slices for UE devices. ### Claim 8 (depends on 7) The method of claim 7, wherein the machine learning classifier is trained to classify UE devices in at least one of: a high density voice service category, a high density video service category, a mobile broadband (MBB) service category, an MBB Ultra-Reliable Low Latency (URLLC) service category, a voice service category, a video service category, an Internet of Things (IoT) data category, an IoT URLLC category, or a Data over Non-Access Stratum (DoNAS) service category. ### Claim 9 (depends on 7) The method of claim 7, wherein determining the plurality of network slices associated with the base station using the machine learning classifier trained to determine network slices for UE devices includes: obtaining a UE device profile for a UE device; and performing a traffic pattern analysis for traffic associated with the UE device. ### Claim 10 (depends on 7) The method of claim 7, wherein the machine learning classifier is trained to classify particular ones of the UE devices serviced by the base station into an Internet of Things data service category based on packet size. ### Claim 11 (depends on 7) The method of claim 7, wherein the machine learning classifier is trained to classify particular ones of the UE devices serviced by the base station into a Data over Non-Access Stratum (DoNAS) service category based on packet size. ### Claim 12 (depends on 7) The method of claim 7, wherein the machine learning classifier is trained to classify particular ones of the UE devices serviced by the base station into a voice, video, or data service category based on bearer type. ### Claim 13 A device comprising: a processor configured to: determine weights for different ones of a plurality of network slices associated with a base station, based on a ratio of a number of user equipment (UE) devices associated with a particular network slice to a total number of UE devices serviced by the base station; compute network slice adjustments for an optimization parameter for different ones of the plurality of network slices; compute an adjustment for the optimization parameter based on the computed network slice adjustments and the determined weights; and adjust the optimization parameter for the base station based on the computed adjustment, wherein the adjusted optimization parameter is used by the base station to manage data traffic for the UE devices serviced by the base station. ### Claim 14 (depends on 13) The device of claim 13, wherein the processor is further configured to: receive an indication of importance for the particular network slice of the plurality of network slices; and adjust a weight associated with the particular network slice, in response to receiving the indication of importance for the particular network slice. ### Claim 15 (depends on 13) The device of claim 13, wherein the processor is further configured to: determine a performance score for a wireless access network associated with the base station based on the adjusted optimization parameter; and revert the optimization parameter to a previous value, when the determined performance score does not result in an improvement over a previous performance score. ### Claim 16 (depends on 13) The device of claim 13, wherein the processor is further configured to: determine positioning and route profiles for the UE devices serviced by the base station, wherein the positioning and route profiles indicate historical position and routes associated with particular ones of the UE devices serviced by the base station; and wherein, when adjusting the optimization parameter for the base station, the processor is further configured to: adjust the optimization parameter for the base station based on the determined positioning and route profiles for the UE devices serviced by the base station. ### Claim 17 (depends on 13) The device of claim 13, wherein the processor is further configured to: determine the plurality of network slices associated with the base station using a machine learning classifier trained to determine network slices for UE devices. ### Claim 18 (depends on 17) The device of claim 17, wherein the machine learning classifier is trained to classify UE devices in at least one of: a high density voice service category, a high density video service category, a mobile broadband (MBB) service category, an MBB Ultra-Reliable Low Latency (URLLC) service category, a voice service category, a video service category, an Internet of Things (IoT) data category, an IoT URLLC category, or a Data over Non-Access Stratum (DoNAS) service category. ### Claim 19 (depends on 17) The device of claim 17, wherein the machine learning classifier is trained to at least one of: classify particular ones of the UE devices serviced by the base station into an Internet of Things data service category based on packet size, classify particular ones of the UE devices serviced by the base station into a Data over Non-Access Stratum (DoNAS) service category based on packet size, or classify particular ones of the UE devices serviced by the base station into a voice, video, or data service category based on bearer type. ### Claim 20 A system comprising: a base station configured to provide a wireless connection to a plurality of user equipment (UE) devices; and a network optimizing device configured to: determine weights for different ones of a plurality of network slices associated with the base station, based on a ratio of a number of user equipment (UE) devices associated with a particular network slice to a total number of UE devices serviced by the base station; compute network slice adjustments for an optimization parameter for different ones of the plurality of network slices; compute an adjustment for the optimization parameter based on the computed network slice adjustments and the determined weights; and adjust the optimization parameter for the base station based on the computed adjustment, wherein the adjusted optimization parameter is used by the base station to manage data traffic for the UE devices serviced by the base station.