Spectrum Effect Inc.

Nokia Solutions and Networks Oy

Eden Rock Communications, LLC

At&t Mobility II LLC

At&t Wireless Services, Inc.

Cingular Wireless Ii, LLC

Clearwire Corporation

Geovisible, Inc.

Opanga Networks, Inc.

Clearwire Legacy LLC

Sr Telecom, Inc.

Sr Telecom & Co., S.e.c.

Eamonn Gormley

Jungnam Yun

Chaz Immendorf

Rekha Menon

David J Ryan

Elliott Hoole

Salvador Garcia, Jr

Jeffrey Paul Harrang

Jose Rodriguez-Sanchez

Charles Immendorf

David B Gibbons

Derek Hilborn

Stephan McLaughlin

Kamyar Moinzadeh

Liang A Hong

Timothy Treptow

Anthony Keng-San Wong

Diego Ayala

Anthony Wong

Kevin M Boyd

Charles James Judson

Vafa Ghazi-Moghadam

Dave Ryan

Neil Stobie

Tim Treptow

Christina Francis

Anh Nguyen

Arun Naidu

Adrian M Yip

Adrian Yip

Ann Nguyen

Rodrigo Vaca

James M Hite

Chad A Pralle

Jesus Alejandro Valdes Valdes

Elliot Hoole

Salvadore Garcia, Jr

David James Ryan

A method for classifying sources of interference provides Operations, Administration, and Management (OAM) data available in a wireless communication network to a trained machine learning model that outputs indications of the types of interference sources exhibited in the OAM data. The OAM data provided to the machine learning model may include per-Physical Resource Block (per-PRB) interference data for a cell, and may further include metadata corresponding to the configuration of the cell.

Classifying RF interference sources using machine learning and operations, administration, and management data

A method for locating a source of interference external to a wireless telecommunications network includes assigning first and second elevation values to each pixel in a grid of pixels that represents an area associated with a plurality of cellular antennas, and calculating at least one probability value that the source of interference external to the wireless telecommunications network is located at an associated pixel by calculating respective probability values for pixels in the grid using the first elevation value and the second elevation value of the associated pixel and vertical gain values for each of the plurality of cellular antennas.

Locating external interference in a wireless network

A method for locating a source of interference external to a wireless telecommunications network includes establishing a grid of pixels that represents an area associated with a plurality of cellular antennas, assigning an elevation value to each pixel in the grid, determining a vertical gain for each of the plurality of cellular antennas, and determining respective probability values for pixels in the grid using the elevation values and the vertical gain, each of the respective probability values representing a probability that the source of interference external to the wireless telecommunications network is located at an associated pixel.

A method and system that separate thermal and background interference noise measurements from interference measurements can determine periodicity of an event using active percentage values. The active percentage value of an interference event may be determined based on unsupervised machine learning on a per-Recording Output Period (ROP) basis. The active percentage of the interference event may be used with other characteristics to help classify the interference source causing the interference. After measuring RF energy at a base station in the network at predetermined intervals over a plurality of consecutive first of time periods, for each time period, a method arranges binned values of the measured energy into at least one cluster, determines whether each cluster of the at least one cluster represents noise or interference, and characterizes the interference, and the method indicates a source of the interference.

Method and system for interference detection and classification

A field interference investigation tool (FIIT) cooperates with a spectrum analysis system to identify the location of a source of non-cellular interference to a cellular telecommunications network. The FIIT may be provided with one or more characteristic of the interference that is used to confirm that interference detected by the FIIT device is relevant to the telecommunications system.

Enhanced system and method for detecting non-cellular RF interference sources to cellular networks

Time division duplexed (TDD) Cellular networks can experience interference in receive times from downlink transmissions by aggressor cells after network changes, or from tropospheric ducting. The interference from the aggressor cells can be efficiently mitigated by determining that the victim cell is being affected by such interference, identifying the aggressor cells, and selectively increasing the guard time between the aggressor cells and the victim cell.

Methods for mitigating interference and maximizing capacity for time division duplex cellular networks

Interference caused by passive intermodulation (PIM) can be automatically detected at receivers in a wireless telecommunications network, and the accuracy of PIM detection can be increased by de-weighting or ignoring time slots in which non-PIM interference is detected at a target receiver.

Enhanced passive intermodulation detection in wireless networks

Tropospheric ducting can cause interference to a wireless telecommunications network from a remote source that would not normally cause such interference to the network. Interference from tropospheric ducting can be determined by analyzing tropospheric ducting factors which, individually or in combination, indicate interference from tropospheric ducting. Future tropospheric ducting interference events can be predicted using forecast data and data from past events.

Detecting tropospheric ducting interference in cellular networks

A system and method that utilize data from co-sited cells that transmit and receive data on frequencies that are licensed to different operators facilitate sharing cellular configuration and performance data without revealing proprietary data to competing operators. Detection of external interference is enhanced by data from different operators.

Secure network information exchange for external interference detection and system for same

A method for determining the location of a source of interference applies data in the vertical dimension to determine the probability of the source being located at various locations, or pixels. The data in the vertical dimension may be used to determine three-dimensional antenna gain for antenna-pixel pairs, and to determine whether a line of sight exists between the antenna-pixel pairs. Probability values are determined for the pixels using one or both of the line of sight information and antenna gain values.

A method of detecting downlink interference in a communications system may include selecting a set of user equipment for measuring interference and generating a downlink interference neighbor list for some or all of the user equipment in the set. Empty resource blocks that are common to base stations on each downlink interference neighbor list are identified, and signals that are received by the set of user equipment during the empty resource blocks are analyzed to detect interference. Certain aspects of the method may be practiced before or after an interference detection period. A system for detecting downlink interference is also provided.

Downlink interference detection using transmission matrices

Remote detection of interference generated by Passive Inter-Modulation (PIM) may be performed by determining intermodulation product information for a plurality of transmitters, receiving downlink power information of the plurality of transmitters, determining, using the intermodulation product information and the downlink power information, a Weighted Downlink Power (WDP) signal for an intermodulation product, and determining, using the WDP signal and uplink interference information, a PIM Detection Assessment (PIMDA) score of the intermodulation product, the value of the PIMDA score corresponding to interference generated by PIM. The WDP signal includes a plurality of expected power values. The remote detection of the interference generated by PIM may be performed by a wireless telecommunications system comprising a receiver and a processor, wherein the uplink interference information is of the receiver.

Remote passive intermodulation detection using nearby transmissions

A method for localizing interference uses data available to wireless communication networks to determine probabilities of a source of external interference being located at a plurality of predetermined locations.

Localization of external interference in wireless networks

Neighbor tier relationships between cells or base stations in a wireless network may be accurately determined by projecting a plurality of points representing the cells or base stations into three dimensional space along with a reference point, creating a plurality of polygons between the plurality of points including the reference point, and removing lines connected to the reference point. Each remaining line represents a first tier neighbor relationship between the two points it connects.

Method and system for neighbor tier counting in three dimensions

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Bothell, WA, United States of America

Eamonn Gormley