Abstract:
Certain aspects of the present disclosure relate to methods and apparatus for network assisted interference cancellation (IC) and interference suppression (IS) for multiple services. According to aspects a user equipment (UE) may determine information regarding system parameters for one or more types of communications services used to transmit potentially interfering signals in one or more neighbor cells, wherein a type of the information determined depends on the type of communications service. The UE may perform interference management using the determined information to cancel or suppress interference caused by the potentially interfering signals.
Abstract:
Methods, systems, and devices for wireless communications are described. A user equipment (UE) may communicate signaling with a network entity, the signaling indicating a first bandwidth size and a second bandwidth size associated with a projection parameter (W1) and a compression parameter (W2), respectively. The second bandwidth size may be less than the first bandwidth size. In some cases, the UE may determine W1 and W2 based on a first machine learning (ML) model and a second ML model, respectively. The UE may project a portion of received channel state information (CSI) associated with the first bandwidth size onto a sub-space defined by W1 and may compress a portion of the projection associated with the second bandwidth size based on W2. The UE may transmit channel state feedback including the projection, the compression of the projection, a compression of W1, a compression of W2, or any combination thereof.
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 presented herein may enable a UE and a network entity to have a common understanding for AI/ML models used in association with AI/ML positioning, thereby improving the performance and efficiency of AI/ML positioning. In one aspect, a UE transmits, to a network entity, a list of UE-supported AI/ML positioning functionalities. The UE receives, from the network entity, an indication of a set of network-supported AI/ML positioning functionalities that are supported by the network entity. The UE transmits, to the network entity, a PRS-based measurement or an estimated location of the UE that is based on using at least one AI/ML model associated with at least one UE-supported AI/ML positioning functionality in the list of UE-supported AI/ML positioning functionalities or at least one network-supported AI/ML positioning functionality in the set of network-supported AI/ML positioning functionalities.
Abstract:
Disclosed are techniques for communication. In an aspect, a radio access network (RAN) node transmits, to a network entity, a set of machine learning positioning capabilities supported by the RAN node, wherein the set of machine learning positioning capabilities includes a list of identifiers of a set of machine learning positioning functionalities supported by the RAN node, wherein the set of machine learning positioning functionalities is associated with a set of machine learning models that support the set of machine learning positioning functionalities, and wherein each machine learning positioning functionality of the set of machine learning positioning functionalities is associated with one or more machine learning models of the set of machine learning models, and transmits, to the network entity, one or more measurements of one or more sounding reference signal (SRS) resources transmitted by a user equipment (UE).
Abstract:
Disclosed are techniques for determining a position of a user equipment (UE). A differential round-trip-time (RTT) based positioning procedure is proposed to determine the UE position. In this technique, the UE position is determined based on the differences of the RTTs between the UE and a plurality of base stations. The differential RTT based positioning procedure has much looser inter-gNodeB timing synchronization requirements than the OTDOA technique and also has much looser group delay requirements than traditional RTT procedures.
Abstract:
Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may receive one or more channel state information (CSI)-reference signals (RSs). The UE may transmit a compressed CSI report that comprises an indication of a plurality of channel metrics, an indication of a channel metric of the plurality of channel metrics comprising a compressed output of a machine learning model having an input as the channel metric, wherein at least one of the plurality of channel metrics is based at least in part on expected orthogonalization of the compressed CSI report at a network node. 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 one or more channel state information (CSI) reference signals (CSI-RSs). The UE may transmit a compressed CSI report via layer 2 signaling, the compressed CSI report being compressed using a machine learning model. Numerous other aspects are described.
Abstract:
An apparatus, method and computer-readable media are disclosed for performing wireless communications. For example, a process for wireless communications is provided. The process can include receiving a first set of operations supported by one or more machine learning models of a network entity, receiving a first set of parameters associated with the first set of operations, wherein the first set of parameters are supported by the one or more machine learning models of the network entity, selecting a machine learning model for performing a first operation of the first set of operations based on the first set of parameters, detecting a change in at least one of: the first operation, or a parameter associated with the first operation, and transmitting an indication to change the first operation based on the detected change.
Abstract:
In an aspect, a radar controller determines a radar slot format that configures transmission of a reference radar signal on a first symbol over a first link from a first base station to a second base station followed by at least one target radar signal on at least one second symbol over at least one second link from the first base station to the second base station, and transmits an indication of the radar slot format to the first base station and the second base station. The first base station transmits, and the second base station receives, the reference radar signal and the at least one target radar signal in accordance with the radar slot format.