UE motion estimate based on cellular parameters
First Claim
1. A user electronics device (UE), comprising:
- at least one antenna for performing wireless communication;
at least one radio coupled to the at least one antenna, wherein the at least one radio is configured to perform cellular communication with a base station;
one or more processors coupled to the at least one radio, wherein the one or more processors and the at least one radio are configured to perform wireless communications using the at least one antenna;
wherein the one or more processors are configured to cause a UE to;
derive a coefficient matrix via a learning model, wherein the one or more processors are further configured to;
provide a first set of parameters to the learning model, wherein the first set of parameters are associated with one or more cellular communication based metrics;
estimate a velocity of the UE based on the learning model;
evaluate the estimated velocity based on a global positioning system velocity of the UE;
adjust values of the coefficient matrix based on the evaluation;
re-estimate the velocity using the provided first set of parameters and the adjusted values of the coefficient matrix; and
normalize the first set of parameters via the adjusted coefficient matrix; and
estimate a first velocity of the UE based on the normalized first set of parameters.
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Accused Products
Abstract
Some embodiments relate to a user equipment device (UE), and associated methods for enabling the UE to estimate velocity of the UE based on cellular parameters. In some embodiments, a first velocity of a UE may be estimated based on a first set of parameters associated with one or more cellular based metrics. Doppler measurements may be performed in response to the first velocity exceeding a velocity threshold for at least a time period. In some embodiments, performing (or conducting) the Doppler measurements may be triggered by (e.g., in response to) the first velocity exceeding the velocity threshold for at least the first time period and receiving an indication from a motion processor of the UE that the UE is in a non-static state. In addition, a second velocity of the UE may be estimated based on the first set of parameters and the Doppler measurements.
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Citations
20 Claims
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1. A user electronics device (UE), comprising:
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at least one antenna for performing wireless communication; at least one radio coupled to the at least one antenna, wherein the at least one radio is configured to perform cellular communication with a base station; one or more processors coupled to the at least one radio, wherein the one or more processors and the at least one radio are configured to perform wireless communications using the at least one antenna; wherein the one or more processors are configured to cause a UE to; derive a coefficient matrix via a learning model, wherein the one or more processors are further configured to; provide a first set of parameters to the learning model, wherein the first set of parameters are associated with one or more cellular communication based metrics; estimate a velocity of the UE based on the learning model; evaluate the estimated velocity based on a global positioning system velocity of the UE; adjust values of the coefficient matrix based on the evaluation; re-estimate the velocity using the provided first set of parameters and the adjusted values of the coefficient matrix; and normalize the first set of parameters via the adjusted coefficient matrix; and estimate a first velocity of the UE based on the normalized first set of parameters. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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10. An apparatus, comprising:
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a memory; and at least one processor in communication with the memory, wherein the at least one processor is configured to; derive a coefficient matrix via a learning model, wherein the at least one processor is further configured to; provide a first set of parameters to the learning model, wherein the first set of parameters are associated with one or more cellular communication based metrics; estimate a velocity of a wireless device associated with the apparatus based on the learning model; evaluate the estimated velocity based on a global positioning system velocity of the wireless device; adjust values of the coefficient matrix based on the evaluation; re-estimate the velocity using the provided first set of parameters and the adjusted values of the coefficient matrix; and normalize the first set of parameters via the adjusted coefficient matrix; and estimate a first velocity of a wireless device based the normalized first set of parameters. - View Dependent Claims (11, 12, 13, 14, 15)
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16. A non-transitory computer-readable memory medium that stores program instructions that, when executed by a wireless user equipment device (UE), cause the UE to:
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derive a coefficient matrix via a learning model, wherein the program instructions are further executable to; provide a first set of parameters to the learning model, wherein the first set of parameters are associated with one or more cellular communication based metrics; estimate a velocity of the UE based on the learning model; evaluate the estimated velocity based on a global positioning system velocity of the UE; adjust values of the coefficient matrix based on the evaluation; re-estimate the velocity using the provided first set of parameters and the adjusted values of the coefficient matrix; and normalize the first set of parameters via the adjusted coefficient matrix; and estimate a first velocity of the UE, wherein the first velocity is based on the normalized first set of parameters. - View Dependent Claims (17, 18, 19, 20)
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Specification