APPARATUS AND METHOD TO ISOLATE VECTORS IN AN ARBITRARILY LARGE N-SPACE

    公开(公告)号:US20200285639A1

    公开(公告)日:2020-09-10

    申请号:US16292509

    申请日:2019-03-05

    Abstract: Aspects of the subject disclosure may include, for example, obtaining a first vector comprising a first plurality of parameters, determining a vector difference between the first plurality of parameters and a second plurality of parameters of a second vector, responsive to the determining, computing a first weighted vector distance based on the vector difference, providing a first representation of the first weighted vector distance to at least one bus, obtaining a second representation of a second weighted vector distance from the at least one bus, comparing the second representation of the second weighted vector distance to the first representation of the first weighted vector distance, and responsive to determining that the second representation of the second weighted vector distance matches the first representation of the first weighted vector distance based on the comparing, setting a first indicator to indicate a first match. Other embodiments are disclosed.

    EVENTS DATA STRUCTURE FOR REAL TIME NETWORK DIAGNOSIS

    公开(公告)号:US20200177469A1

    公开(公告)日:2020-06-04

    申请号:US16208023

    申请日:2018-12-03

    Abstract: Aspects of the subject disclosure may include, for example, a method that includes detecting events relating to user equipment on a communication network, collecting first event data including event times and locations, and collecting second event data regarding second event dimensions determined at least in part by the event type. The method also includes generating, for each of the event types, an event data structure associated with the user, based on the first event data and second event data. The event data structures are concatenated to generate an event history flow associated with the user; the event history flow is analyzed to identify causal events for a detected event. The method also includes generating a model for performance of the user equipment based on the causal events to predict a future event, and identifying potential adjustments to the communication network to prevent that event. Other embodiments are disclosed.

    ROUTING OPTIMIZATION BASED ON HISTORICAL NETWORK MEASURES

    公开(公告)号:US20200112905A1

    公开(公告)日:2020-04-09

    申请号:US16155410

    申请日:2018-10-09

    Abstract: In one example, a first plurality of health scores and a second plurality of health cores are calculated. The first plurality of health scores quantifies the health of a plurality of nodes in a telecommunication service provider network, where the plurality of nodes represents connectivity points in the network. The second plurality of health scores quantifies the health of a plurality of links connecting the plurality of nodes. A priority score is calculated that quantifies an importance of a traffic demand. A route over the plurality of nodes and the plurality of links is generated, based at least in part on the first plurality of health scores, the second plurality of health scores, and the priority score. The route delivers the traffic demand from a source to a destination in a manner that meets a need of the traffic demand without exceeding the need by more than the threshold.

    Base station identification based location quality data determination

    公开(公告)号:US10206195B2

    公开(公告)日:2019-02-12

    申请号:US15923809

    申请日:2018-03-16

    Abstract: Determining a location quality based on base station identification is disclosed. The location quality can be based on an error attributed to a location determined based on historical data related to an identified base station. Application of supplemental data to the historical base station data can improve location quality by reducing the error. Supplemental data can comprise Voronoi data, geographic data, historical UE density data, historical UE timing advance data, or combinations thereof. Voronoi data can be associated with an area less than a service area of the base station. Geographic data can indicate areas where UEs are not likely to be located. UE density data can indicate probably UE locations. Timing advance data can indicate annular regions where a UE should be located. As such, the supplemental data can constrain a location determined for a UE and correspondingly can reduce error associated with the location.

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