Intel Corporation

Apple Inc.

Yatish Mishra

Mats Gustav Agerstam

Mateo Guzman

Sindhu Pandian

Pranav Sanghadia

Shubhangi Rajasekhar

Troy Willes

Jorge Ivonnet

Methods, apparatus, systems and articles of manufacture to trigger calibration of a sensor node using machine learning are disclosed. An example apparatus includes a machine learning model trainer to train a machine learning model using first sensor data collected from a sensor node. A disturbance forecaster is to, using the machine learning model and second sensor data, forecast a temporal disturbance to a communication of the sensor node. A communications processor is to transmit a first calibration trigger in response to a determination that a start of the temporal disturbance is forecasted and a determination that a first calibration trigger has not been sent.

Methods and apparatus to trigger calibration of a sensor node using machine learning

Aspects include an apparatus and a method for performing Second Order Input Intercept Point (IIP2) calibration of a Digital-to-Analog (DAC) during a full-duplex mode receive operation. In some aspects, a plurality of correlation values are obtained, indicating an amount of IMD energy of an RF signal, wherein the correlation values are associated with IIP2DAC values of a DAC. In some aspects, the apparatus can calculate a mixer bias value, based on the correlation values, and adjust a bias value of a mixer according to the determined bias value. The apparatus can obtain the correlation values, calculate the bias value, and adjust the bias value of the mixer during the full-duplex mode receive operation. In some aspects, the apparatus can thus improve IIP2 of the mixer and reduce IMD energy in a receive signal, during the receive operation, without the need of standby or factory calibration.

Method and apparatus for IIP2 calibration

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Tempe, AZ, United States of America

Yatish Mishra