Abstract: Certain aspects of the present disclosure provide techniques for adaptively tuning a wireless data transmission system in an electronic device, including: generating a plurality of measurements of an element of the wireless data transmission system in the electronic device; generating a determined use case for the electronic device based on a use case determination model and the plurality of measurements; determining one or more antenna settings associated with the determined use case; tuning the wireless data transmission system based on the one or more antenna settings; and transmitting data via the wireless data transmission system using the one or more antenna settings.

Abstract: Methods, systems, and devices for wireless communications are described. In some systems, a user equipment (UE) may implement a dynamic threshold for antenna switching. The dynamic threshold may improve the percentage of time that a UE operates using an “optimal” antenna (e.g., an antenna with the highest reference signal received power (RSRP)) as compared to a static threshold. For example, a UE may communicate with another device using a first antenna. The UE may track a current communication measurement for the first antenna (e.g., an RSRP value) and may perform an antenna switching test based on the measurement. For the test, the UE may update the dynamic threshold for antenna switching based on a machine learning agent, where the agent may include a neural network that determines the updated dynamic threshold value. The UE may perform a comparison with the dynamic threshold to determine whether to switch operating antennas.

Abstract: Systems and methods for augmenting data by performing reject inference are disclosed. In one embodiment, the disclosed process trains an auto-encoder based on a subset of known labeled rows (e.g., non-default loan applicants). The process then infers labels for unlabeled rows using the auto-encoder (e.g., label some rows as non-default and some as default). The process then trains a machine learning model based on the known labeled rows and the inferred labeled rows. Applicant data is then processed by this new machine learning model to determine if a loan applicant is likely to default. If the loan applicant is not likely to default, the loan applicant is funded. For example, the loan applicant may be mailed a physical working credit card. However, if the loan applicant is likely to default, the loan applicant is rejected. For example, the loan applicant may be mailed a physical adverse action letter.

Abstract: Methods, systems, and devices for wireless communications are described. In some systems, a user equipment (UE) may implement a dynamic threshold for antenna switching. The dynamic threshold may improve the percentage of time that a UE operates using an “optimal” antenna (e.g., an antenna with the highest reference signal received power (RSRP)) as compared to a static threshold. For example, a UE may communicate with another device using a first antenna. The UE may track a current communication measurement for the first antenna (e.g., an RSRP value) and may perform an antenna switching test based on the measurement. For the test, the UE may update the dynamic threshold for antenna switching based on a machine learning agent, where the agent may include a neural network that determines the updated dynamic threshold value. The UE may perform a comparison with the dynamic threshold to determine whether to switch operating antennas.

Abstract: Methods, systems, and devices for wireless communications are described. In some systems, a user equipment (UE) may implement a dynamic threshold for antenna switching. The dynamic threshold may improve the percentage of time that a UE operates using an “optimal” antenna (e.g., an antenna with the highest reference signal received power (RSRP)) as compared to a static threshold. For example, a UE may communicate with another device using a first antenna. The UE may track a current communication measurement for the first antenna (e.g., an RSRP value) and may perform an antenna switching test based on the measurement. For the test, the UE may update the dynamic threshold for antenna switching based on a machine learning agent, where the agent may include a neural network that determines the updated dynamic threshold value. The UE may perform a comparison with the dynamic threshold to determine whether to switch operating antennas.

Abstract: Certain aspects of the present disclosure provide techniques for adaptively tuning a wireless data transmission system in an electronic device, including: generating a plurality of measurements of an element of the wireless data transmission system in the electronic device; generating a determined use case for the electronic device based on a use case determination model and the plurality of measurements; determining one or more antenna settings associated with the determined use case; tuning the wireless data transmission system based on the one or more antenna settings; and transmitting data via the wireless data transmission system using the one or more antenna settings.

Abstract: Methods, systems, and devices for wireless communications are described. In some systems, a user equipment (UE) may implement a dynamic threshold for antenna switching. The dynamic threshold may improve the percentage of time that a UE operates using an “optimal” antenna (e.g., an antenna with the highest reference signal received power (RSRP)) as compared to a static threshold. For example, a UE may communicate with another device using a first antenna. The UE may track a current communication measurement for the first antenna (e.g., an RSRP value) and may perform an antenna switching test based on the measurement. For the test, the UE may update the dynamic threshold for antenna switching based on a machine learning agent, where the agent may include a neural network that determines the updated dynamic threshold value. The UE may perform a comparison with the dynamic threshold to determine whether to switch operating antennas.

Abstract: Aspects of the present disclosure relate to techniques for switching antennas in devices that have multiple antennas for communicating via aggregation of multiple carriers.

Abstract: Aspects of the present disclosure relate to techniques for switching antennas in devices that have multiple antennas for communicating via aggregation of multiple carriers.