Abstract: Embodiments herein provide various apparatuses and techniques to for enhanced handling of receiver desensitization, “desense,” in electronic devices that is caused by an operation of the device. In an embodiment, sensitivity of a receiver affected by desense may be determined based on receiver sensitivity without desense and a desense offset value. Desense mitigation measures may be applied based on the strength of the signal received by the receiver being less than the receiver sensitivity affected by desense. The desense mitigation measures may include deactivating the device operation that causes desense, enabling the device operation that causes desense only during certain times, and enabling antenna diversity. In some embodiments, various power and signal-to-noise ratio thresholds may be used for conditional application of different desense mitigation measures.

Abstract: Embodiments herein provide various apparatuses and techniques to for enhanced handling of receiver desensitization, “desense,” in electronic devices that is caused by an operation of the device. In an embodiment, sensitivity of a receiver affected by desense may be determined based on receiver sensitivity without desense and a desense offset value. Desense mitigation measures may be applied based on the strength of the signal received by the receiver being less than the receiver sensitivity affected by desense. The desense mitigation measures may include deactivating the device operation that causes desense, enabling the device operation that causes desense only during certain times, and enabling antenna diversity. In some embodiments, various power and signal-to-noise ratio thresholds may be used for conditional application of different desense mitigation measures.

Abstract: Disclosed subject matter relates to supervised machine learning including a method and system for generating synchronized labelled training dataset for building a learning model. The training data generation system determines a timing advance factor to achieve time synchronization between User Equipment (UE) and network nodes, by signalling the UE to initiate playback of the multimedia content based on the timing advance factor. The training data generation system receives network Key Performance Indicator (KPI) data from the network nodes and a user experience data from the UE, concurrently, for the streamed multimedia content, and performs timestamp based correlation to generate a synchronized labelled training dataset for building a learning model.

Abstract: A method and system for federating location of a point of emergency across a plurality of networks is disclosed. The method includes receiving, by a Location Service Orchestrator (LSO), an emergency alert from a User Equipment (UE), wherein the UE is communicatively coupled with a first network from the plurality of networks. The method further includes determining, by the LSO, a location of the UE based on the received emergency alert. The method includes refining, by the LSO, the determined location based on a network initiated location determination mechanism of the first network, when the accuracy of determined location of the UE is below a pre-defined threshold for locational accuracy. The method further includes federating, by the LSO, the refined location of the UE across the plurality of networks.

Abstract: Disclosed subject matter relates to supervised machine learning including a method and system for generating synchronized labelled training dataset for building a learning model. The training data generation system determines a timing advance factor to achieve time synchronization between User Equipment (UE) and network nodes, by signalling the UE to initiate playback of the multimedia content based on the timing advance factor. The training data generation system receives network Key Performance Indicator (KPI) data from the network nodes and a user experience data from the UE, concurrently, for the streamed multimedia content, and performs timestamp based correlation to generate a synchronized labelled training dataset for building a learning model.