179 results about "Cell site" patented technology

Disclosed are various embodiments for dynamic availability zones in radio-based networks. In one embodiment, excess resource capacity on a radio access network (RAN)-enabled edge server in a cloud provider network is determined. The RAN-enabled edge server is located at a cell site and is configured to perform distributed unit (DU) and / or centralized unit (CU) functions for a RAN. The excess resource capacity is offered as part of a cellular capacity zone that is generally available to customers of the cloud provider network.

Systems, devices, and automated processes are described to provide collection of wireless network status data, such as a 5G or other mobile network, and to automatically respond to queries regarding the status of the network. Systems and automated processes may obtain status data from a plurality of network components, for example a radio unit (RU), a distributed unit (DU), and a centralized unit (CU) associated with a cell site, ingest the obtained status data, including processing the status data with a machine learning model system, store the ingested status data in a data store, receive a user query related to the network, ingest the received user query using the MLM system, retrieve a result corresponding to the ingested query from the data store using the MLM system, generate, using the MLM system, a summary of the retrieved status data, and present the generated summary via a user interface.

Network downtime determination and reporting technologies are disclosed. An example method for determining downtime associated with a communications network includes detecting an event that triggers update of a downtime accumulation estimate associated with the communications network, and determining real-time status of a subset of cell sites that is potentially affected by the event. The example method also includes updating the downtime accumulation estimate based on the real-time status of the subset, and comparing the updated downtime accumulation estimate with one or more criteria for reporting the same.

Systems, devices, and automated processes are described to provide collection of wireless network status data, such as for a 5G or other mobile network, and to automatically created user interactable dashboards providing various status information regarding the operation of the network. Systems and automated processes may obtain status data from a plurality of network components, for example radio units (RU), distributed units (DU), and centralized units (CU) associated with a cell sites, determine an operational state for each of the network components based on the obtained data, aggregate the determined operational states to determine an operational state of the cell sites, generate a dashboard having an indication of the operational states, provide the dashboard to a user interface, receive a user selection from the dashboard, and provide a report detailing the determined operational state.

A processing system may apply a data set comprising utilization metrics of a cells of a cell sector to a throughput prediction model to obtain a first predicted throughput for the cell sector for a designated future time period and for all cells being in an active state. The processing system may generate a first modified data set simulating a first cell being placed in an inactive state, by distributing utilization metrics of the first cell over at least one additional cell, and may apply the first modified data set to the throughput prediction model to obtain a second predicted throughput. The processing system may then determine that the second predicted throughput meets a threshold throughput that is based on the first predicted throughput, and transmit at least one instruction to place the first cell in the inactive state for the designated future time period in response to the determining.

Aspects of the subject disclosure may include, for example, accessing device usage data that can be generated when end user devices connect with the network; and analyzing the device usage data according to identification information of the deployable cell site, which enables determining or estimating a location of the deployable cell site. In one embodiment, this analysis can be based on device usage reports and network performance data. A performance report and / or location map can be generated for an area associated with the deployable cell site. Other embodiments are disclosed.

Aspects of the subject disclosure may include, for example, training a cell-level machine learning model to predict a likelihood of a cell site in a cellular network having service issues that impact customers of the cellular network, training a user equipment (UE) level machine learning model using output information from the cell-level machine learning model and historical information about UE-level performance metrics, receiving, from a customer associated with a UE device operating on the cellular network, information about a service degradation experienced by the customer on the UE device, providing the information about the service degradation to the UE-level machine learning model; and receiving, from the UE-level machine learning model, information identifying a source of the service degradation. Other embodiments are disclosed.

Aspects of the subject disclosure may include, for example, obtaining data regarding an environment associated with a cell site, identifying one or more coverage pivots from a plurality of coverage locations in the environment, utilizing one or more machine learning (ML) models to determine emission directions for the one or more coverage pivots based at least in part on locations of the one or more coverage pivots and the cell site in the environment, resulting in determined emission directions, and causing ray tracing simulation to be performed using the determined emission directions, wherein the ray tracing simulation enables first signal coverage estimates to be derived for the one or more coverage pivots and used to extrapolate second signal coverage estimates for a remainder of the plurality of coverage locations. Other embodiments are disclosed.

An automated system provides end-to-end (E2E) orchestration for machine learning (ML)-enabled and software-defined network (SDN)-enabled reactive and predictive (e.g., 5G) cell-on-drone (COD) dispatch and deployment. The system may be configured to detect or predict traffic surges (e.g., relative to typical or baseline levels) in a given cell site and orchestrate provisioning of resources, including COD deployment, to address the surge. COD deployment may involve one or more clusters or colonies of drones equipped with low-powered cellular radio access (or small cell) nodes, where each cluster includes (or is led by) one or more SDN-equipped drones that provide intelligent SDN-on-drone (SOD) functionality.

Aspects herein provide a system, media, and methods for / of an application that utilizes and leverages machine learning techniques, infrastructure information (e.g., existing and planned cell sites, lease agreement sites, fiber-optic networks, geographic landmarks), and collected telecommunication data, to accurately identify and determine specific solutions and specific locations for those solutions to be deployed in a geographic area. In embodiments, the application autonomously identifies an optimized specific solution type for various clusters of poor-service coverages areas within the geographic areas. The application also determines a precise location for deployment of each optimized specific solution type for each cluster.

Aspects of the subject disclosure may include, for example, training a cell-level machine learning model to predict a likelihood of a cell site in a cellular network having service issues that impact customers of the cellular network, training a user equipment (UE) level machine learning model using output information from the cell-level machine learning model and historical information about UE-level performance metrics, receiving, from a customer associated with a UE device operating on the cellular network, information about a service degradation experienced by the customer on the UE device, providing the information about the service degradation to the UE-level machine learning model; and receiving, from the UE-level machine learning model, information identifying a source of the service degradation. Other embodiments are disclosed.

A quality of experience QoE server associated with a home wireless communication service network. The QoE server comprises a QoE application that, when executed by the QoE server, receives an indication of a location of a user equipment (UE), wherein the UE is subscribed to receive wireless communication service with the home network and that the UE is in a roaming operation mode, searches a geo-location database using the indication of the location of the UE, based on searching the geo-location database, determines that the home network or a preferred network is located proximate to the indication of the location of the UE, and sends a request to the UE to perform a refresh operation, wherein the UE initiates a scan of cell site frequencies, detects a cell site associated with the home network or the preferred network, and attaches wirelessly to the cell site.

An example process may collect performance data from a cell site of an open radio access network (RAN) in an area of interest (AOI). The cell site may include sectors and the sectors comprising cells. Busy-hour indicators may be determined based on the performance data from the cell site. The busy-hour indicators may be determined by applying a percentile method to outliers in the performance data. A subscriber growth model in the AOI can be forecast for a forecast period. The busy-hour indicators can be extrapolated using the subscriber growth model to generate forecast indicators for the forecast period. A gain function can be applied to the forecast indicators to generate revised forecast indicators. A capacity breach at a cell or a sector of the cell site in the AOI can be detected in response to an indicator from the revised forecast indicators exceeding a capacity threshold.

Systems and methods of balancing traffic perform or comprise, in a cell site configured to provide network services in a plurality of layers, respective ones of the plurality of layers having different bandwidth characteristics: receiving a traffic data for the cell site; determining a cell radius for respective ones of the plurality of layers; and in response to a determination that the plurality of layers are coincident, distributing a plurality of users to the plurality of layers based on the traffic data.

A wireless communication device. The wireless communication device comprises a radio transceiver; a first processor; a first non-transitory memory; and a subscriber identity module (SIM). The SIM comprises a second processor, a second non-transitory memory storing network keys that the radio transceiver uses to authenticate to a cell site, and an applet stored in the second non-transitory memory. When executed by the second processor, the applet sends a request to know where the wireless communication device is to the first processor, receives an answer from the first processor containing a cell global identifier (CGI) of a first cell site that the radio transceiver is attached to and containing a received signal strength of the first cell site, determines that the received signal strength of the first cell site is below a predefined threshold, and sends a refresh command to the first processor.

A system is provided for at least one of testing and validating at least one Open Radio Access Network (O-RAN) remote radio head (RRH) present for installation at an installation site having no network connectivity to the RRH available, which system includes a network emulator configured to at least one of test and validate at least one of a device parameter and a functional parameter of the at least one RRH when selectively coupled to the at least one RRH. The network emulator is configured to be selectively coupled to a user equipment (UE) configured for displaying the at least one of the device parameter and the functional parameter of at least one RRH, and wherein the network emulator is one of 1) directly coupled to the at least one RRH, or 2) connected to the at least one RRH via a cell site router (CSR).

A test device tests conditions associated with a fronthaul in an Open Radio Access Network (O-RAN). The test device can field test an O-RAN radio unit (O-RU) installed at a cell site by emulating an O-RAN distributed unit (O-DU) in the O-RAN connected to the O-RU via the fronthaul of the O-RAN. The testing includes executing managing plane (M-plane) and synchronization plane (S-plane) messaging to test management session establishment, device setting testing, and master clock synchronization testing.

Systems and methods for utilizing distributed computing and storage resources intelligently select processing and / or storage resources of cell sites, data centers and / or other computing nodes and tap into these available processing and data storage resources to harness these resources on behalf of other remote devices to increase the amount of computing and storage data resources to perform tasks and store data for various services. The system predicts demand and usage and dynamically selects and adjusts utilization of computing resources to meet the demand and utilize otherwise idle systems. To improve latency and reduce network congestion by avoiding adding network traffic over longer and more complex routes, the system provides resources to particular remote devices based on the geographic proximity of the computing node to the particular remote device requesting the resources.

Aspects of the subject disclosure may include, for example, communicating management plane (M-plane) data at a first port of a radio unit (RU), the RU being one RU of a set of radio units (RUs) at a cell site of a network operator, the M-plane data related to management and control of the RU, wherein the communicating M-plane data comprises communicating the M-plane data with a distributed unit (DU) over an M-plane segment, and initiating communication of the M-plane data at a second port of the RU, the second port configured for data communication over an alternate M-plane segment to a second RU of the set of RUs for forwarding the M-plane data to the DU over a second M-plane segment between the second RU and the DU, wherein the initiating communication is in response to an interruption in the communicating M-plane data.Other embodiments are disclosed.

Systems and methods for automating radio unit tasks is provided. An example method includes establishing, by one or more processors, a connection between an RU automation component and an RU at a cell site; obtaining, by the one or more processors, RU data associated with the RU; wherein the RU data includes data that identifies one or more RU configuration settings; determining, by the one or more processors, to perform one or more RU operations associated with the RU; and causing, by the one or more processors, the one or more operations to execute.

An auto-commissioning server may be used in the auto-commissioning of virtualized Radio Access Networks (vRANs) by receiving a plurality of parameters from a site controller of a cell site, an inventory, and a plurality of network entities; automatically generating a day zero configuration based on the received plurality of parameters; deploying the network function(s) of the vRANs into a cloud server based on the automatically generated day zero configuration; receiving a power-on notification message from a RU of the cell site, wherein the power-on notification message may indicate the RU of the cell site is ready for radio signal transmission; and activating the deployed network function(s) of the vRANs before transmitting a radio signal from the RU of the cell site.

The invention relates to an intelligent optimization method for a non-cellular station address for sensing fusion, and the method comprises the steps: S1, building a mathematical relation model between communication performance and an access point station address based on a logarithmic distance path loss model; s2, on the basis of a bistatic system path loss model, constructing a mathematical relationship model between the sensing performance and the access point site; and S3, modeling an access point site optimization problem in the cellular-free sensing fusion system as a Markov decision process, constructing a state space and an action space, and calculating a state of the access point site according to a mathematical relationship model between communication performance and the access point site and a mathematical relationship model between sensing performance and the access point site. Fusing to obtain a reward function of a Markov decision process; and S4, solving the Markov decision process by adopting a flexible action-evaluation deep reinforcement learning algorithm to obtain an optimal access point deployment strategy. Compared with the prior art, the method has the advantages of improving the site selection accuracy, widening the application range, improving the site selection effect stability and the like.

An inventory system comprises one or more mini-cell sites, one or more reader devices to read tags, and a control system. The mini-cell site comprises radio equipment configured to broadcast power signals within an inventory environment. The control system transmits a first instruction to the mini-cell site to set a configuration parameter at the mini-cell site to a first frequency channel based on mini-cell site capability data and inventory environment data, and transmits a second instruction to the reader device to set a network setting at the reader device to a second frequency channel based on reader device capability data and the inventory environment data. The first frequency channel is different from the second frequency channel.

Disclosed are various embodiments for managing excess capacity in radio access network (RAN) edge systems. In one embodiment, a RAN-enabled edge server is located at a cell site, is configured to execute distributed unit (DU) and / or centralized unit (CU) functions for a RAN, and includes a physical layer accelerator specialized for DU physical layer communication. A computing device is configured to determine that the RAN-enabled edge server has excess resource capacity beyond a quantity necessary to execute the DU / CU functions for the RAN, determine a demand for the excess resource capacity from a cloud provider network, and determine whether to offer the excess resource capacity to third-party customers of the cloud provider network based at least in part on the demand.

Disclosed are various embodiments for predicting excess capacity for radio access network (RAN) edge systems. In one embodiment, resource consumption data is generated by monitoring resource consumption of a plurality of implementations of a distributed unit (DU) and / or centralized unit (CU) used in RANs. A maximum level of resource consumption is predicted for a particular implementation of the DU / CU to be used in a particular RAN based at least in part on the resource consumption data. Excess resource capacity is predicted on a RAN-enabled edge server on which the particular implementation of the DU / CU is deployed for a cell site of the RAN based at least in part on the maximum level of resource consumption.

The disclosed embodiments include a method for classifying cell sites of a virtual network that simulates merging a donor network with an anchor network. The method can include obtaining cell site information of the anchor network and the donor network, simulating the virtual network that merges the donor network with the anchor network, and classifying each donor site based on a value associated with the donor site relative to nearest anchor sites. Any anchor site that is located a threshold distance from the donor site is a nearest anchor site. A donor site is classified for decommissioning when the value is less than or equal to a threshold or the donor site is classified for retaining when the value is greater than the threshold. The method can further include causing an output of an indication of any donor site that is classified for decommissioning or retaining.

Systems and methods of balancing traffic perform or comprise, in a cell site configured to provide network services in a plurality of layers, respective ones of the plurality of layers having different bandwidth characteristics: receiving a traffic data for the cell site; determining a cell radius for respective ones of the plurality of layers; and in response to a determination that the plurality of layers are coincident, distributing a plurality of users to the plurality of layers based on the traffic data.

Methods, media, and systems are provided for controlling cell site selection by a user device in a telecommunications network. A System Information Block (SIB) message (e.g., an SIB Type 1 message) is transmitted (e.g., by a cell site) for receipt by the user device for cell site selection. In some aspects, the SIB message is transmitted while the user device is camping on a serving cell of a base station other than the cell site. The SIB message comprises information that identifies a type of the cell site. The type of the cell site is a cell site other than a macro cell, such as a small cell for example. Upon selection of the cell site by the user device based on the SIB message and the type of the cell site, the user device is provided access to a telecommunications service.

Aspects of the subject disclosure may include, for example, a device, including: a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations of identifying a public land mobile network (PLMN) identifier associated with a communications network; determining a tracking area code (TAC) range of a plurality of cell sites in a radio access network (RAN) associated with the PLMN identifier; identifying transport network circuits of a transport network connecting the plurality of cell sites with a core network of the communications network; and displaying a user interface comprising a graphical representation of the core network, the transport network and the RAN. Other embodiments are disclosed.

A core network is configured to receive a first message indicative of a trigger event at a mobile device, in which the trigger event occurs when the mobile device is initiating an RFID communication with an RFID tag over a frequency channel, in which the mobile device is also configured to communicate with a cell site associated with the core network over the frequency channel, and perform an interference mitigation action on a cellular communication destined for the mobile device over the frequency channel, in which the interference mitigation action comprises temporarily caching the cellular communication destined for the mobile device over the frequency channel or rerouting the cellular communication destined for the mobile device over another frequency channel.