Abstract: Certain aspects of the present disclosure provide techniques for packet buffering. A method that may be performed by a receiving node includes dynamically determining one or more time durations to buffer packets. The one or more time durations can be different than a time duration of a configured timer for buffering the packets. The receiving node may input one or more parameters to a machine learning algorithm and obtain, as output of the machine learning algorithm based on the input one or more parameters, one or more time durations to buffer packets. The receiving node buffers packets for the determined one or more time durations. The receiving node may use machine learning to dynamically determine the one or more time durations to buffer packet. The buffering may be at a radio link control (RLC) reassembling buffer and/or a packet data convergence protocol (PDCP) buffer.

Abstract: Methods, systems, and devices for wireless communications are described. A system may support techniques for triggering user equipment (UE) assistance information (UAI) based on mobility, wake up signal (WUS) configuration, or both. In some cases, a UE may communicate with a network node (e.g., a base station) on a first channel according to a discontinuous reception (DRX) configuration. The UE may determine that a speed of the UE satisfies a mobility threshold and may transmit UAI requesting one or more updated DRX configuration parameters based on the speed of the UE satisfying the mobility threshold. Additionally or alternatively, the UE may receive signaling configuring the UE to monitor for WUSs and may transmit UAI requesting one or more updated DRX configuration parameters based on the WUS configuration. The UE may receive control signaling configuring the DRX configuration with the one or more requested parameters in response to the UAI.

Abstract: A method of wireless communication, by a user equipment (UE), includes setting up a secondary cell group (SCG) with a second radio access technology (RAT) that differs from a first RAT associated with a master cell group (MCG). The method also includes communicating wirelessly via the secondary cell group and the master cell group. The method further includes predicting a radio link failure (RLF) for the secondary cell group based on multiple inputs to a machine learning model. The method still further includes routing data transmission from the secondary cell group to the master cell group, after predicting the SCG RLF.

Abstract: Methods, systems, and devices for wireless communications are described. In some examples, a wireless communications system may support machine learning and may configure a user equipment (UE) for machine learning. The UE may transmit, to a base station, a request message that includes an indication of a machine learning model or a neural network function based at least in part on a trigger event. In response to the request message, the base station may transmit a machine learning model, a set of parameters corresponding to the machine learning model, or a configuration corresponding to a neural network function and may transmit an activation message to the UE to implement the machine learning model and the neural network function.

Abstract: Disclosed are techniques for wireless communication. In an aspect, a wireless communication device may obtain information regarding communication between the set of communication devices and a non-terrestrial network (NTN) and communicate with the NTN. The wireless communication device may communicate with the NTN directly based on the wireless communication device being identified as one of one or more selected wireless communication devices for direct communication with the NTN. The wireless communication device may communicate with the NTN indirectly via a peer-to-peer communication network and the one or more selected wireless communication devices based on the wireless communication device being not identified as any of the one or more selected wireless communication devices for direct communication with the NTN.

Abstract: A delta configuration is signaled for handover of a wireless communication device (e.g., a user equipment, UE) from a first form of connectivity to a second form of connectivity. For example, a UE with master cell group (MCG) connectivity may be handed-over to multiple radio access technology-dual connectivity (MR-DC). In some examples, a UE with standalone (SA) connectivity may be handed-over to non-standalone (NSA) connectivity (e.g., dual connectivity). In conjunction with this handover the UE may be signaled as to whether the UE is to reuse a configuration from the first connectivity mode during the second connectivity mode.

Abstract: Methods, systems, and devices for wireless communications are described. In some examples, a wireless communications system may support machine learning and may configure a user equipment (UE) for machine learning. The UE may transmit, to a base station, a request message that includes an indication of a machine learning model or a neural network function based at least in part on a trigger event. In response to the request message, the base station may transmit a machine learning model, a set of parameters corresponding to the machine learning model, or a configuration corresponding to a neural network function and may transmit an activation message to the UE to implement the machine learning model and the neural network function.

Abstract: In some implementations, a mobile device may determine a set of target orientations of the mobile device in which an antenna lobe of the mobile device is pointed toward the satellite, the set of target orientations based on: an orientation of the antenna lobe relative to the mobile device, and a location of the satellite relative to the mobile device. The mobile device may determine a current orientation of the mobile device. The mobile device may provide, at a user interface (UI) of the mobile device, guidance for rotating the mobile device from the current orientation to an orientation within the set of target orientations.

Abstract: In some implementations, a mobile device may perform a plurality of signal strength measurements of a radio frequency (RF) signal transmitted by a satellite, wherein performing the plurality of signal strength measurements occurs over a period of time during which the mobile device is subject to a movement. The mobile device may determine, for each signal strength measurement of the plurality of signal strength measurements, a respective orientation of the mobile device corresponding to when the respective signal strength measurement was performed. The mobile device may determine a target orientation for the satellite-based communications based at least in part on a value of a particular signal strength measurement of the plurality of signal strength measurements and the respective orientation of the mobile device corresponding to the particular signal strength measurement. The mobile device may provide guidance for rotating the mobile device to the target orientation.

Abstract: Certain aspects of the present disclosure provide techniques for dynamically determining an uplink data split threshold for communicating using one or more radio link control (RLC) entities of a split bearer configuration. A method that may be performed by a user equipment (UE) includes inputting a first set of parameters to a machine learning algorithm, obtaining, as an output of the machine learning algorithm based at least in part on the inputted first set of parameters, a value for a data split threshold, and transmitting the data using at least one of a first RLC entity or the second RLC entity based on the value for the data split threshold and the amount of data the apparatus has to transmit.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may communicate on a first set of carriers in a first frequency range (FR) and a second set of carriers in a second FR. The UE may detect a radio link control (RLC) discontinuity on at least one of the first set of carriers or the second set of carriers. The UE may identify one or more FRs, of the first FR and the second FR, in which the RLC discontinuity occurred. The UE may transmit an RLC status report based at least in part on the identified one or more FRs. Numerous other aspects are described.

Abstract: Methods, systems, and devices for wireless communications are described. A system may support techniques for triggering user equipment (UE) assistance information (UAI) based on mobility, wake up signal (WUS) configuration, or both. In some cases, a UE may communicate with a network node (e.g., a base station) on a first channel according to a discontinuous reception (DRX) configuration. The UE may determine that a speed of the UE satisfies a mobility threshold and may transmit UAI requesting one or more updated DRX configuration parameters based on the speed of the UE satisfying the mobility threshold. Additionally or alternatively, the UE may receive signaling configuring the UE to monitor for WUSs and may transmit UAI requesting one or more updated DRX configuration parameters based on the WUS configuration. The UE may receive control signaling configuring the DRX configuration with the one or more requested parameters in response to the UAI.

Abstract: Methods, systems, and devices for wireless communication in a multi-connectivity user equipment (UE) are described. The UE may communicate with one or more base stations via a first radio access technology (RAT). The UE may determine whether the UE is in a selected state. The selected state may correspond to one or more modes of operation (e.g., a doze mode, a relaxed doze mode, an active WiFi communication mode, a low battery mode). The UE may disable communication via a second RAT in response to determining that the UE is in the selected state. The UE may continue to communicate via the first RAT.

Abstract: Various embodiments include methods for autonomous beam switching by a wireless device. A processor of the wireless device may measure signal parameters of signals received from a first synchronization signal block (SSB) beam of a base station monitored by the wireless device and other SSB beams of the base station, determine whether a difference in measured signal parameters of signals received from the first SSB beam and another SSB beam of the base station satisfies a signal quality difference threshold, and autonomously switching to the second SSB beam as the serving beam in response to determining that the difference in the measured signal parameters of signals received from the first SSB beam and a second SSB beam satisfies the signal quality difference threshold. The signal quality difference threshold may be listed in a table in memory or determined via machine learning.

Abstract: A method of wireless communication, by a user equipment (UE), includes setting up a secondary cell group (SCG) with a second radio access technology (RAT) that differs from a first RAT associated with a master cell group (MCG). The method also includes communicating wirelessly via the secondary cell group and the master cell group. The method further includes predicting a radio link failure (RLF) for the secondary cell group based on multiple inputs to a machine learning model. The method still further includes routing data transmission from the secondary cell group to the master cell group, after predicting the SCG RLF.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may communicate on a first set of carriers in a first frequency range (FR) and a second set of carriers in a second FR. The UE may detect a radio link control (RLC) discontinuity on at least one of the first set of carriers or the second set of carriers. The UE may identify one or more FRs, of the first FR and the second FR, in which the RLC discontinuity occurred. The UE may transmit an RLC status report based at least in part on the identified one or more FRs. Numerous other aspects are described.

Abstract: Certain aspects of the present disclosure provide techniques for packet buffering. A method that may be performed by a receiving node includes dynamically determining one or more time durations to buffer packets. The one or more time durations can be different than a time duration of a configured timer for buffering the packets. The receiving node may input one or more parameters to a machine learning algorithm and obtain, as output of the machine learning algorithm based on the input one or more parameters, one or more time durations to buffer packets. The receiving node buffers packets for the determined one or more time durations. The receiving node may use machine learning to dynamically determine the one or more time durations to buffer packet. The buffering may be at a radio link control (RLC) reassembling buffer and/or a packet data convergence protocol (PDCP) buffer.

Abstract: Methods, systems, and devices for wireless communications are described. In some examples, a wireless communications system may support machine learning and may configure a user equipment (UE) for machine learning. The UE may transmit, to a base station, a request message that includes an indication of a machine learning model or a neural network function based at least in part on a trigger event. In response to the request message, the base station may transmit a machine learning model, a set of parameters corresponding to the machine learning model, or a configuration corresponding to a neural network function and may transmit an activation message to the UE to implement the machine learning model and the neural network function.

Abstract: Aspects disclosed in the detailed description include power saving techniques in computing devices. In particular, as data is received by a modem processor in a computing device, the data is held until the expiration of a modem timer. The data is then passed to an application processor in the computing device over a peripheral component interconnect express (PCIe) interconnectivity bus. On receipt of the data from the modem processor, the application processor sends data held by the application processor to the modem processor over the PCIe interconnectivity bus. The application processor also has an uplink timer. If no data is received from the modem processor before expiration of the uplink timer, the application processor sends any collected data to the modem processor at expiration of the uplink timer. However, if data is received from the modem processor, the uplink timer is reset.

Abstract: Aspects disclosed in the detailed description include power saving techniques in computing devices. In particular, as data is received by a modem processor in a computing device, the data is held until the expiration of a modem timer. The data is then passed to an application processor in the computing device over a peripheral component interconnect express (PCIe) interconnectivity bus. On receipt of the data from the modem processor, the application processor sends data held by the application processor to the modem processor over the PCIe interconnectivity bus. The application processor also has an uplink timer. If no data is received from the modem processor before expiration of the uplink timer, the application processor sends any collected data to the modem processor at expiration of the uplink timer. However, if data is received from the modem processor, the uplink timer is reset.