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 handling user equipment (UE) mobility for a cross-node artificial intelligence (AI) and/or machine learning (ML) session in a radio access network (RAN). A method of wireless communication by a first network entity includes obtaining, from a second network entity, an indication of a handover for a UE; obtaining, from a third network entity, an indication of first cross-node machine learning information associated with a cross-node machine learning session between the UE and the third network entity; providing, to the second network entity, an indication acknowledging the handover; and relaying communications between the UE and the third network entity for the cross-node machine learning session.

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: Certain aspects of the present disclosure provide techniques for performing cross-node machine learning operations in a radio access network. An example method of wireless communication by a first network entity includes providing, to a second network entity, an indication of cross-node machine learning information used for a cross-node machine learning session between the first network entity and a user equipment (UE); obtaining machine learning information associated with the UE; and controlling the cross-node machine learning session based at least in part on the machine learning information.

Abstract: Methods, systems, and devices for wireless communications are described. In a service-based network, groups of core network services may share a user equipment (UE) credential or network subscription. Signaling between a UE and a service-based network (e.g., via a distributed unit) enables the UE to establish a service context for communications with multiple core network services in a same service group. A UE may receive control signaling from the network indicating service groups offered by the service-based network. The control signaling may indicate the services included in each service group and how to access the service group. The UE may transmit an access request indicating a selected service group to the network, and in response, the network may indicate a service context for communicating with the services included in the service group. The UE may use the same service context to access the different services in the service group.

Abstract: Methods, systems, and devices for wireless communications are described. A network entity may obtain a first message indicating one or more artificial intelligence (AI)/machine learning (ML) capabilities of a user equipment (UE) and may further obtain, from an AI/ML service, a second message indicating one or more AI/ML service capabilities of the AI/ML service. The network entity may output a control message indicating one or more cross-node AI/ML configurations based on the one or more AI/ML capabilities of the UE and the one or more AI/ML service capabilities of the AI/ML service. The one or more cross-node AI/ML configurations may configure AI/ML functions of the UE for use with the AI/ML service. In some examples, life cycle management (LCM) procedures may be triggered by a UE, and the UE may transmit signaling associated with the LCM procedures (e.g., a request for LCM signaling or an indication of LCM).

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may receive, from a primary cell or a primary secondary cell (PSCell), configuration information for a conditional PSCell addition or change (CPAC), the configuration information indicating configurations for one or more candidate PSCells. The UE may detect a failure, associated with the conditional PSCell addition or change, associated with a first target PSCell from the one or more candidate PSCells. The UE may perform an action to recover the CPAC based at least in part on detecting the failure. 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 initiate one of a conditional handover (CHO) procedure or a conditional primary secondary cell addition/change (CPAC) procedure. The UE may perform a response action with regard to a configuration of the other of the CHO procedure or the CPAC procedure based at least in part on initiating the one of the CHO procedure or the CPAC procedure. Numerous other aspects are described.

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: 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 a plurality of source symbols in accordance with applying a forward error correction encoding to the plurality of PDCP packets, wherein the plurality of source symbols are non-segmented outer coding source symbols. The transmitting device may transmit the plurality of source symbols. Numerous other aspects are described.

Abstract: This disclosure provides systems, methods, and devices for wireless communication that enhanced system information delivery schemes. In a first aspect, a method of wireless communication includes receiving, by a user equipment (UE), access system information (SI) from a network device; transmitting, by the UE, a service SI request based on the received access SI, the service SI request indicating a request for particular service SI data; and receiving, by the UE, the particular service SI data, the particular service SI data including one or more SI blocks (SIBs), scheduling information for the one or more SIBs, or both. Other aspects and features are also claimed and described.

Abstract: Methods, systems, and devices for wireless communications are described. In some examples, a user equipment (UE) may support dual connectivity. That is, the UE may establish communications with a master node and a secondary node of a wireless communications network. The master node may correspond to a master cell group (MCG) and the secondary node may correspond to a secondary cell group (SCG). In some examples, as described herein, the UE operating in dual connectivity may collect data for optimization of the wireless communications network or upon detecting a failure associated with the master cell group or the SCG and transmit the collected data to a network entity (e.g., one of the master node or the secondary node), where the collected data is based on the SCG being in a deactivated state. In some examples, upon receiving the collected data, the network entity may attempt to recover from the failure.

Abstract: Aspects described herein relate to receiving, from a network node, a list of supported models or model structures (MS) identifiers (IDs) per machine learning function name (MLFN) or machine learning feature (MLF) at the network node, updating a capability at the UE to an updated capability based on the list of supported models or MS IDs per MLFN or MLF at the network node, and downloading, at the UE and from a model repository, one or more models or MSs per MLFN or MLF based on the updated capability and available resources at the UE. Other aspects relate to transmitting the list of supported models or MS IDs and configuring use of a model or MS ID for a particular MLFN or MLF.

Abstract: An apparatus, method and computer-readable media are disclosed for performing wireless communications. For example, a process can include: transmitting, to a security service of the wireless network, a first service access request for accessing a service of the wireless network, wherein the service is separate from the security service; receiving, from the security service in response to the first service access request, service security information for accessing the service; deriving a service key based on the service security information; transmitting, to the service, a second service access request, the second service access request encoded based on the derived service key; and establishing a first security context with the service based on the derived service key.

Abstract: Methods, systems, and devices for wireless communication are described. A network entity may configure a user equipment (UE) to report applicability information associated with artificial intelligence (AI) and/or machine learning (ML) functionalities and/or models. The applicability information indicates an applicability of a functionality and/or model per AI/ML-enabled feature or feature group. Based on the applicability information and a handover of the UE from a source network entity to a target network entity, the target network entity may configure a functionality and/or model. Additionally, or alternatively, the UE may be configured with a reference configuration and one or more additional configurations corresponding to respective AI/ML functionalities and/or models. The UE may apply an additional configuration based on a handover to the target network entity, the additional configuration satisfying an applicability condition, or both.

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: Methods, systems, and devices for wireless communications are described. In one aspect, a user equipment (UE) may be provided with wireless local area network (WLAN) identifiers associated with a public land mobile network (PLMN) for determining WLAN access points that support control plane termination at a fifth generation (5G) RAN for the PLMN. In another aspect, a network node may encapsulate system information in a broadcast message for transmission from a WLAN access point to a UE, and the UE may be configured to receive the system information from the WLAN access point in one or more modification periods. In yet another aspect, a network node may encapsulate a paging message for a broadcast or unicast transmission from a WLAN access point to a UE. In yet another aspect, UEs and network nodes may be configured to map traffic characteristics of traffic for a 5G bearer to WLAN access categories.

Abstract: A reference signal transmission method includes: determining, at a network entity, a signal configuration of a transmission signal that comprises at least one decoding portion and at least one reference signal portion, where the signal configuration comprises a transmission schedule of the at least one decoding portion and the at least one reference signal portion, where each of the at least one decoding portion comprises decoding information to decode a corresponding one of the at least one reference signal portion, where each of the at least one decoding portion is scheduled in one or more first symbols and the corresponding one of the at least one reference signal portion is scheduled in one or more second symbols, and where none of the one or more first symbols is before any of the one or more second symbols; and transmitting, from the network entity, the transmission signal.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may transmit capability information that indicates support for one or more model combinations of machine learning (ML) models, wherein the capability information further indicates one or more performance parameters of an ML model of the ML models with respect to a model combination of the one or more model combinations that includes the ML model. The UE may receive one or more indications to use one or more of the ML models based at least in part on the capability information. Numerous other aspects are described.

Abstract: Aspects of this disclosure relate generally to mobility in a service-based architecture. Some aspects more specifically relate to path switching in a service-based architecture. In some examples, a distributed node may exchange signaling with a service server to configure and execute a path switch in connection with handover of a user equipment to the distributed node. For example, the distributed node may communicate with the service server directly. As another example, the distributed node may communicate with a mobility service to execute the path switch. In some examples, aspects described herein provide for a service server to buffer data and provide data forwarding in connection with the path switch.