Abstract:
Apparatus and methods are described for identifying candidate cells on at least one frequency, where each of the candidate cells is associated with a cell quality, storing information related to each of the candidate cells in a candidate list, sorting the candidate list, and decoding a master information block (MIB) and one or more system information blocks (SIBs) for a subset of the candidate cells based on the sorting.
Abstract:
Methods, systems, and devices for wireless communications are described. A wireless device may establish a radio resource control (RRC) connection over a wireless local area network (WLAN) link. For example, the wireless device may transmit a packet to an access point or WLAN termination that includes an RRC container including an RRC message for establishing (e.g., setting up, resuming) a connection with a centralized unit (CU). In some examples, the wireless device may additionally or alternatively be configured with dual connectivity over a WLAN link, including the addition, modification, and release of nodes associated with different radio access technologies. In such cases, the CU may configure connectivity with the WLAN, where the CU may configure a port number for each radio bearer via an RRC message transmitted over a direct link with the wireless device (e.g., via a distributed unit) or via the WLAN link.
Abstract:
Certain aspects of the present disclosure provide techniques for managing cross-node artificial intelligence (AI) and/or machine learning (ML) operations in a radio access network (RAN). An example method of wireless communication by a first network entity includes obtaining machine learning input data associated with a user equipment (UE); providing, to a second network entity, an indication of machine learning output data generated using the machine learning input data; and providing, to the second network entity, control signaling for a cross-node machine learning session between the UE and the first network entity based at least in part on one or more performance indicators associated with the cross-node machine learning session.
Abstract:
Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a transmitting device may obtain a plurality of packet data convergence protocol (PDCP) packets. The transmitting device may generate an outer coding block in accordance with assembling the plurality of PDCP packets. The transmitting device may segment the outer coding block into a plurality of outer coding symbols in accordance with an outer coding symbol size. The transmitting device may apply a forward error correction encoding to the outer coding block. The transmitting device may transmit the plurality of outer coding symbols in accordance with applying the forward error correction encoding to the outer coding block. Numerous other aspects are described.
Abstract:
Methods, systems, and devices for wireless communication are described. A network entity may monitor a performance of a machine learning (ML) model or ML model-based functionality associated with a user equipment (UE). The UE may receive one or more control messages that indicate a life cycle management (LCM) operation for the ML model or ML model-based functionality. The one or more control messages may include an indication of whether the LCM operation is based on the performance of the MIL model or ML model-based functionality. In some examples, the indication may include or be an example of a performance report associated with the performance of the ML model or ML model-based functionality. The UE may perform the LCM operation for the ML model or ML model-based functionality. The UE or the network entity may transmit the indication to a server associated with the ML model or ML model-based functionality.
Abstract:
Aspects of the present disclosure provide apparatus, methods, processing systems, and computer readable mediums for a nested conditional mobility procedure. In some cases, a method for wireless communications by a UE generally includes receiving configuration information configuring the UE for conditional handover (CHO) from a source master node (S-MN) to a target master node (T-MN) and for conditional primary secondary cell (PSCell) addition or change (CPAC) and performing a nested procedure based on an evaluation of conditions for both CHO and CPAAC in accordance with the configuration information.
Abstract:
Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may determine an antenna switching capability for the UE that relates to a capability of the UE to switch an antenna for a plurality of bands that are included in a band combination of a set of band combinations supported by the UE, wherein at least two bands of the plurality of bands correspond to a first radio access technology (RAT) and a second RAT respectively. The UE may signal the set of band combinations and the antenna switching capability to a base station. Numerous other aspects are provided.
Abstract:
Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may obtain generalization information associated with a model, a model structure (MS), or a parameter set (PS) associated with the model. The UE may initiate a connection to a network node. The UE may filter the model, the MS, or the PS based at least in part on the generalization information. The UE may transmit UE capability information to the network node, based at least in part on filtering the model, the MS or the PS, that indicates whether the model, the MS, or the PS is applicable, available, or supported by the UE. Numerous other aspects are described.
Abstract:
Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may receive, from a network node, an indication of one or more data radio bearers between the UE and the network node that are configured for application data unit (ADU) traffic. The UE may communicate, with an application server, one or more ADU traffic flows through the network node using the one or more data radio bearers. Numerous other aspects are provided.
Abstract:
A protocol stack architecture for processing machine learning (ML) data includes a ML layer to manage ML data communication with a network device. The ML layer is coupled to multiple ML training blocks, and ML and inference blocks for multiple neural networks, and an analog data communications stack coupled to the ML layer. The analog data communications stack has an upper media access control analog (MAC-A) layer coupled to the ML layer and configured to store data for each neural network, a lower MAC-A layer coupled to the upper MAC-A layer and configured to segment and reassemble analog ML data, and an analog physical layer coupled to the lower MAC-A layer and configured to communicate analog data with the network device. The architecture includes a digital data communications stack coupled to the ML layer and the lower MAC-A layer and configured to manage digital communications with the network device.