The present disclosure provides radio-frequency (RF) systems that can detect the presence of RF signals received by the system, as well as determine characteristics such as the operating frequency of RF signals, the type of RF source that transmitted each RF signal, and/or the location of each RF source with high precision and sensitivity while using low cost, scalable electronics that are versatile enough for deployment in a variety of environments. Such systems can employ a network of RF sensors that can coordinate in response to communication with a computer to perform any such detection and/or determination using trained models executed onboard the RF sensors and/or the computer. RF signals may have unique characteristics when received at one or more RF sensors that may be detected using trained models described herein, even in high noise or non-line of sight (LOS) environments and with low cost, low resolution RF receiver hardware.

The present disclosure provides radio-frequency (RF) systems that can detect the presence of RF signals received by the system, as well as determine characteristics such as the operating frequency of RF signals, the type of RF source that transmitted each RF signal, and/or the location of each RF source with high precision and sensitivity while using low cost, scalable electronics that are versatile enough for deployment in a variety of environments. Such systems can employ a network of RF sensors that can coordinate in response to communication with a computer to perform any such detection and/or determination using trained models executed onboard the RF sensors and/or the computer. RF signals may have unique characteristics when received at one or more RF sensors that may be detected using trained models described herein, even in high noise or non-line of sight (LOS) environments and with low cost, low resolution RF receiver hardware.

A UE may identify a plurality of local model update elements associated with an updated local machine learning model. The updated local machine learning model may have been generated based on a global machine learning model received from a base station and a local dataset. The UE may identify one or more local model update elements of the plurality of local model update elements for update element dropping based on at least one of a channel gain, a PAPR specification, an RF emission specification, or an RF condition. The UE may transmit, to the base station over a multiple access channel via analog signaling, at least some local model update elements of the plurality of local model update elements based on dropping of the identified one or more local model update elements.

A UE may identify a plurality of local model update elements associated with an updated local machine learning model. The updated local machine learning model may have been generated based on a global machine learning model received from a base station and a local dataset. The UE may identify one or more local model update elements of the plurality of local model update elements for update element dropping based on at least one of a channel gain, a PAPR specification, an RF emission specification, or an RF condition. The UE may transmit, to the base station over a multiple access channel via analog signaling, at least some local model update elements of the plurality of local model update elements based on dropping of the identified one or more local model update elements.

A computer-implemented method for setting RF transmit power of a wireless device includes obtaining power response data for a power detector of the wireless device, the power response data providing a relationship between power detector readings from the power detector and measured transmit power of a transmitter of the wireless device; identifying a target power detector reading value corresponding to a target transmit power based at least in part on the power response data; initializing a transmitter gain of the transmitter to an initial transmitter gain value; obtaining a power detector reading value from the power detector; determining that a difference between the power detector reading value and the target power detector reading value is greater than a tolerance margin; and adjusting a transmitter gain value of the transmitter in a direction of the difference between the power detector reading value and the target power detector reading value.

Methods, systems, and devices for wireless communications are described. The method includes receiving, from a base station, control signaling identifying a measurement configuration for one or more non-linear estimation measurements of reference signals associated with a power amplifier configuration of the base station, receiving, from the base station, the reference signals on a set of resources identified by the measurement configuration, performing one or more non-linear estimation measurements associated with the power amplifier configuration of the base station based on the received reference signals, and transmitting, to the base station according to the measurement configuration, a measurement report based on the one or more non-linear estimation measurements.

A communication system for SAR (Specific Absorption Rate) reduction includes an RF (Radio Frequency), an antenna element, a coupler, and a controller. The RF module provides RF power. The antenna element is excited by the RF module. The coupler is coupled between the RF module and the antenna element. The controller receives antenna information from the coupler. The controller adjusts the level of RF power according to the antenna information.

A communication system for SAR (Specific Absorption Rate) reduction includes an RF (Radio Frequency), an antenna element, a coupler, and a controller. The RF module provides RF power. The antenna element is excited by the RF module. The coupler is coupled between the RF module and the antenna element. The controller receives antenna information from the coupler. The controller adjusts the level of RF power according to the antenna information.

Techniques are provided which may be implemented using various methods and/or apparatuses in a mobile device to address maximum permissible exposure (MPE) proximity sensor failure. A mobile device may include a maximum permissible exposure (MPE) sensor control unit to actively monitor signals associated with proper operation of the MPE proximity sensors. Upon detecting an anomaly in any of these signals, such as a value drop below a given threshold, an MPE sensor control Unit will inform an AP (application processor, or other processor or controller) which in turn trigger display of a warning message on the display of the mobile device or the issuance of other warnings such an audible or tactile alert to inform the end user about the maximum permissible exposure (MPE) proximity sensor malfunction and/or notify the end use of a condition resulting in deactivation of the 5G new radio transceiver.

Techniques are provided which may be implemented using various methods and/or apparatuses in a mobile device to address maximum permissible exposure (MPE) proximity sensor failure. A mobile device may include a maximum permissible exposure (MPE) sensor control unit to actively monitor signals associated with proper operation of the MPE proximity sensors. Upon detecting an anomaly in any of these signals, such as a value drop below a given threshold, an MPE sensor control Unit will inform an AP (application processor, or other processor or controller) which in turn trigger display of a warning message on the display of the mobile device or the issuance of other warnings such an audible or tactile alert to inform the end user about the maximum permissible exposure (MPE) proximity sensor malfunction and/or notify the end use of a condition resulting in deactivation of the 5G new radio transceiver.