Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may receive configuration information associated with a first sounding reference signal (SRS) resource set, including a first quantity of SRS resources, and a second SRS resource set, including a second quantity of SRS resources. The UE may receive downlink control information (DCI) scheduling a spatial division multiplexing communication, the DCI indicating a first one or more SRS resources, associated with a first set of layers, and a second one or more SRS resources, associated with a second set of layers, from the first SRS resource set and/or the second SRS resource set based on: a dynamic switching indicator, a first SRS resource indicator (SRI), a second SRI, the first quantity of SRS resources, the second quantity of SRS resources, and/or a maximum rank. Numerous other aspects are described.

Abstract: Aspects of the disclosure relate to power control for reference signals, such as a sounding reference signal (SRS) in an uplink (UL) dense deployment. A user equipment (UE) may receive, via a transceiver, an indication of a power spectral density for a sounding reference signal (SRS) from a base station. The UE may then transmit, via the transceiver, the SRS at a power level that is based on the power spectral density. The base station may then receive, from an uplink (UL) receive point via the communication interface, an indication of a measured power of the SRS received by the UL receive point from the UE. The base station may then transmit, to the UE, an UL transmitter configuration based on the indication of the measured power of the SRS. Other aspects, embodiments, and features are also claimed and described.

Abstract: Methods, systems, and devices for wireless communications are described. A user equipment (UE) may be configured to indicate to a base station a distribution of transmit antennas and receive antennas across a set of antenna panels, and the base station may configure resource sets for reference signal transmissions from the antenna panels based on the indicated distribution. In a first example, the UE may be configured to indicate a respective number of transmit antennas and receive antennas at each antenna panel. In a second example, the UE may be configured to indicate a sum of transmit antennas and a sum of receive antennas across all antenna panels, along with information on how the transmit antennas and receive antennas are distributed. Based on the indication, the base station may configure resource sets for reference signal transmission on sets of ports in resources of the resource sets.

Abstract: Disclosed are systems and methods including software processes executed by a server that detect audio-based synthetic speech (“deepfakes”) in a call conversation. The server applies an NLP engine to transcribe call audio and analyze the text for anomalous patterns to detect synthetic speech. Additionally or alternatively, the server executes a voice “liveness” detection system for detecting machine speech, such as synthetic speech or replayed speech. The system performs phrase repetition detection, background change detection, and passive voice liveness detection in call audio signals to detect liveness of a speech utterance. An automated model update module allows the liveness detection model to adapt to new types of presentation attacks, based on the human provided feedback.

Abstract: Disclosed are systems and methods including software processes executed by a server that detect audio-based synthetic speech (“deepfakes”) in a call conversation. The server applies an NLP engine to transcribe call audio and analyze the text for anomalous patterns to detect synthetic speech. Additionally or alternatively, the server executes a voice “liveness” detection system for detecting machine speech, such as synthetic speech or replayed speech. The system performs phrase repetition detection, background change detection, and passive voice liveness detection in call audio signals to detect liveness of a speech utterance. An automated model update module allows the liveness detection model to adapt to new types of presentation attacks, based on the human provided feedback.

Abstract: Disclosed are systems and methods including software processes executed by a server that detect audio-based synthetic speech (“deepfakes”) in a call conversation. The server applies an NLP engine to transcribe call audio and analyze the text for anomalous patterns to detect synthetic speech. Additionally or alternatively, the server executes a voice “liveness” detection system for detecting machine speech, such as synthetic speech or replayed speech. The system performs phrase repetition detection, background change detection, and passive voice liveness detection in call audio signals to detect liveness of a speech utterance. An automated model update module allows the liveness detection model to adapt to new types of presentation attacks, based on the human provided feedback.

Abstract: Disclosed are systems and methods including software processes executed by a server that detect audio-based synthetic speech (“deepfakes”) in a call conversation. The server applies an NLP engine to transcribe call audio and analyze the text for anomalous patterns to detect synthetic speech. Additionally or alternatively, the server executes a voice “liveness” detection system for detecting machine speech, such as synthetic speech or replayed speech. The system performs phrase repetition detection, background change detection, and passive voice liveness detection in call audio signals to detect liveness of a speech utterance. An automated model update module allows the liveness detection model to adapt to new types of presentation attacks, based on the human provided feedback.

Abstract: Disclosed are systems and methods including software processes executed by a server that detect audio-based synthetic speech (“deepfakes”) in a call conversation. The server applies an NLP engine to transcribe call audio and analyze the text for anomalous patterns to detect synthetic speech. Additionally or alternatively, the server executes a voice “liveness” detection system for detecting machine speech, such as synthetic speech or replayed speech. The system performs phrase repetition detection, background change detection, and passive voice liveness detection in call audio signals to detect liveness of a speech utterance. An automated model update module allows the liveness detection model to adapt to new types of presentation attacks, based on the human provided feedback.

Abstract: Disclosed are systems and methods including software processes executed by a server that detect audio-based synthetic speech (“deepfakes”) in a call conversation. The server applies an NLP engine to transcribe call audio and analyze the text for anomalous patterns to detect synthetic speech. Additionally or alternatively, the server executes a voice “liveness” detection system for detecting machine speech, such as synthetic speech or replayed speech. The system performs phrase repetition detection, background change detection, and passive voice liveness detection in call audio signals to detect liveness of a speech utterance. An automated model update module allows the liveness detection model to adapt to new types of presentation attacks, based on the human provided feedback.

Abstract: Disclosed are systems and methods including software processes executed by a server that detect audio-based synthetic speech (“deepfakes”) in a call conversation. The server applies an NLP engine to transcribe call audio and analyze the text for anomalous patterns to detect synthetic speech. Additionally or alternatively, the server executes a voice “liveness” detection system for detecting machine speech, such as synthetic speech or replayed speech. The system performs phrase repetition detection, background change detection, and passive voice liveness detection in call audio signals to detect liveness of a speech utterance. An automated model update module allows the liveness detection model to adapt to new types of presentation attacks, based on the human provided feedback.

Abstract: Disclosed are systems and methods including software processes executed by a server that detect audio-based synthetic speech (“deepfakes”) in a call conversation. The server applies an NLP engine to transcribe call audio and analyze the text for anomalous patterns to detect synthetic speech. Additionally or alternatively, the server executes a voice “liveness” detection system for detecting machine speech, such as synthetic speech or replayed speech. The system performs phrase repetition detection, background change detection, and passive voice liveness detection in call audio signals to detect liveness of a speech utterance. An automated model update module allows the liveness detection model to adapt to new types of presentation attacks, based on the human provided feedback.

Abstract: Apparatus, methods, and computer-readable media for facilitating multi-cluster control resource sets for downlink control channel repetition are disclosed herein. An example method for wireless communication at a UE includes receiving a configuration for a CORESET having a first RB set associated with a first TCI state and a second RB set associated with a second TCI state. The example method also includes monitoring for a control signal in the first RB set and the second RB set. Another example method includes receiving a configuration for an SSS associated with a first CORESET having a first TCI state and a second CORESET having a second TCI state. The example method also includes monitoring for a control signal based on the configuration for the SSS.

Abstract: A method of wireless communication performed by a user equipment (UE) includes: receiving, from a base station (BS), a multi-transport block (TB) repetition configuration indicating a first number of repetitions and a second number of repetitions; receiving, from the BS, downlink control information (DCI) indicating a beam pattern including a plurality of beam directions; communicating, with the BS based on the multi-TB repetition configuration and the beam pattern, a first TB and a second TB associated with a same scheduling grant, wherein the communicating the first TB and the second TB includes: communicating the first number of repetitions for the first TB; and communicating the second number of repetitions for the second TB.

Abstract: Methods, systems, and devices for wireless communications are described. A user equipment (UE) may be configured to indicate to a base station a distribution of transmit antennas and receive antennas across a set of antenna panels, and the base station may configure resource sets for reference signal transmissions from the antenna panels based on the indicated distribution. In a first example, the UE may be configured to indicate a respective number of transmit antennas and receive antennas at each antenna panel. In a second example, the UE may be configured to indicate a sum of transmit antennas and a sum of receive antennas across all antenna panels, along with information on how the transmit antennas and receive antennas are distributed. Based on the indication, the base station may configure resource sets for reference signal transmission on sets of ports in resources of the resource sets.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may set, for frequency hopping of multiple physical uplink shared channel (PUSCH) repetitions of multiple transport blocks (TBs), a first frequency for a first frequency hop and a second frequency for a second frequency hop. The UE may transmit the PUSCH repetitions with frequency hopping such that the PUSCH repetitions alternate between the first frequency hop and the second frequency hop. Numerous other aspects are described.

Abstract: Certain aspects of the present disclosure provide techniques for detecting a potential collision between a scheduled uplink transmission and a synchronization signal block (SSB) and transmitting the scheduled uplink transmission. For example, in cases of inter-cell multi-transmission and reception points (multi-TRP), a user equipment (UE) may not be capable of simultaneously transmitting and receiving on the multiple serving cells. A first serving cell may indicate to the UE of SSB indices that are actually transmitted for a second serving cell. If there is a slot collision between one of the SSB to be transmitted and a scheduled uplink transmission, the UE may not be able to handle such collision. The present disclosure provides techniques for detecting a potential collision between a scheduled uplink transmission and one of the SSBs and transmitting the scheduled uplink transmission when one or more conditions are met.

Abstract: Embodiments described herein provide for evaluating call metadata and certificates of inbound calls for authentication. The computer identifies a service provider indicated by the SPID and/or the ANI (or other identifier) of the metadata and identifies a service provider indicated by the SPID and/or ANI (or other identifier) of the certificate, then compares identities of the service providers and/or compares the data values associated with the service providers (e.g., SPIDs, ANIs). Based on this comparison, the computer determines whether the service provider that signed the certificate is first-party signer (e.g., carrier) for the ANI or a third-party signer that is signing certificates as the first-party signer for the ANI.

Abstract: Embodiments described herein provide for systems and methods for verifying authentic JIPs associated with ANIs using CLLIs known to be associated with the ANIs, allowing a computer to authenticate calls using the verified JIPs, among various factors. The computer builds a trust model for JIPs by correlating unique CLLIs to JIPs. A malicious actor might spoof numerous ANIs mapped to a single CLLI, but the malicious actor is unlikely to spoof multiple CLLIs due to the complexity of spoofing the volumes of ANIs associated with multiple CLLIs, so the CLLIs can be trusted when determining whether a JIP is authentic. The computer identifies an authentic JIP when the trust model indicates that a number of CLLIs associated with the JIP satisfies one or more thresholds. A machine-learning architecture references the fact that the JIP is authentic as an authentication factor for downstream call authentication functions.

Abstract: A failsafe mechanism for installing and removing temporary instrumentation during a runtime of an application. Initially, an application is configured with a baseline set of instrumented components such as methods. Additional instrumentation is then deployed in the application, such as to diagnose a performance problem. The failsafe mechanism ensures that the additional instrumentation is automatically removed, even when there is an interruption in a communication link to the application, a computing device failure, a software failure, or some other type of failure, which renders it impossible to manually roll back the instrumentation from a remote user interface. The failsafe mechanism can be provided using callbacks between the computing devices which detect when a connection is unexpectedly lost or closed. Termination of one callback can cascade to one or more other callbacks. The instrumentation rollback can involve reloading un-instrumented byte code of the application.

Abstract: Techniques for analyzing software in which un-instrumented components can be discovered and conditionally instrumented during a runtime of the software. Initially, software such as an application can be configured with a baseline set of instrumented components such as methods. As the application runs, performance data gathered from the instrumentation may indicate that the performance of some methods is below expectations. To analyze this, any methods which are callable from a method at issue are discovered, such as by inspecting the byte code of loaded classes in a JAVA Virtual Machine (JVM). To limit and focus the diagnosis, the instrumentation which is added to the discovered components can be conditional, so that the instrumentation is executed only in a specified context. The context can involve, e.g., a specified sequence of components in which a discovered component is called, and/or transaction data in which a discovered component is called.