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US 12,013,473 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.
Claims20
A method, comprising:
- receiving, by a processing device, a first indication that a first estimated error value does not satisfy a first predetermined threshold value, the first estimated error value related to (i) a first number of signal paths and (ii) a first estimated characteristic of a radio frequency (RF) signal observed by an antenna array;
- computing, by the processing device, responsive to receiving the first indication that the first estimated error value does not satisfy the first predetermined threshold value, a second estimated error value related to (i) a second number of signal paths larger than the first number of signal paths and (ii) a second estimated characteristic of the RF signal;
- receiving, by the processing device, a second indication that the second estimated error value satisfies a second predetermined threshold value;
- and
- outputting, by the processing device, the second estimated characteristic of the RF signal.
2 more elements
A system comprising:
- a transceiver configured to receive a plurality of radio frequency (RF) signal power measurements observed by an antenna array;
- and
- a processor coupled to the transceiver, the processor to:
- receive a first indication that a first estimated error value does not satisfy a first predetermined threshold value, the first estimated error value related to (i) a first number of signal paths and (ii) a first estimated characteristic of the plurality of RF signal power measurements observed by the antenna array;
- compute, responsive to receiving the first indication that the first estimated error value does not satisfy the first predetermined threshold value, a second estimated error value related to (i) a second number of signal paths larger than the first number of signal paths and (ii) a second estimated characteristic of the plurality of RF signal power measurements;
- receive a second indication that the second estimated error value satisfies a second predetermined threshold value;
- and
- output the second estimated characteristic of the plurality of RF signal power measurements.
5 more elements
A method comprising:
- training a first machine learning model using a first dataset produced in a first signal propagation environment, the first signal propagation environment having a first number of signal paths measurable by an antenna array;
- training a second machine learning model using a second dataset produced in a second signal propagation environment, the second signal propagation environment having a second number of signal paths measurable by an antenna array, wherein the second number of signal paths is larger than the first number of signal paths, wherein using the first machine learning model requires less processing than using the second machine learning model.
Dependencies are inferred from the claim text.