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US 11,531,080 B2Granted
Leveraging spectral diversity for machine learning-based estimation of radio frequency signal parameters
AssigneeCypress Semiconductor Corporation
InventorsAidan Smyth, Victor Simileysky, Kiran Uln
Life of this patent
Estimating the term…
Abstract
An example method for estimating the angle-of-arrival (AoA) and other parameters of radio frequency (RF) signals that are received by an antenna array comprises: receiving a plurality of radio frequency (RF) signal power measurements by a plurality of antenna elements at a plurality of RF channels; computing, by applying a machine learning model to the plurality of RF signal power measurements, an estimated RF signal parameter value; and outputting the RF signal parameter value.
Claims19
A method, comprising:
- receiving, by a processing device, a plurality of radio frequency (RF) signal power measurements by a plurality of antenna elements at a plurality of RF channels;
- computing, by the processing device applying a first machine learning model to the plurality of RF signal power measurements, an estimated RF signal parameter value;
- responsive to determining that an estimated error value exceeds a predetermined threshold, re-computing the estimated RF signal parameter value by applying a second machine learning model, wherein the second machine learning model corresponds to a larger number of RF signal multi-paths than the first machine learning model;
- and
- outputting, by the processing device, the estimated RF signal parameter value.
2 more elements
A system comprising:
- a transceiver configured to couple to an antenna comprising a plurality of antenna elements to receive a plurality of radio frequency (RF) signal power measurements at a plurality of frequency channels;
- and
- a processor coupled to the transceiver, the processor to:
- select a machine learning model from a sequence of machine learning models, wherein a first machine learning model of the sequence has been trained on a first training data set produced in a first signal propagation environment that includes a first number of signal paths, wherein a second machine learning model of the sequence has been trained on a second training data set produced in a second signal propagation environment that includes a second number of signal paths, and wherein the second number of signal paths exceeds the first number of signal paths;
- compute, by applying the selected machine learning model to the plurality of RF signal measurements, an estimated RF signal parameter value;
- and
- responsive to determining that a parameter estimation error is less than or equal a predetermined error threshold value, output the estimated RF signal parameter value.
4 more elements
A device comprising:
- a transceiver configured to couple to an antenna comprising a plurality of antenna elements to receive a plurality of radio frequency (RF) signal power measurements at a plurality of frequency channels at a plurality of RF channels;
- and
- a processor coupled to the transceiver, the processor to:
- compute, by applying a first machine learning model to the plurality of RF signal measurements at the plurality of RF channels, an estimated RF signal parameter value;
- responsive to determining that an estimated error value exceeds a predetermined threshold, re-compute the estimated RF signal parameter value by applying a second machine learning model, wherein the second machine learning model corresponds to a larger number of RF signal multi-paths than the first machine learning model;
- and
- responsive to determining that a parameter estimation error is less than or equal a predetermined error threshold value, output the estimated RF signal parameter value.
4 more elements
Dependencies are inferred from the claim text.