Abstract:
Certain aspects of the present disclosure relate to methods and apparatus for employing a neighboring cell's assistance information for interference mitigation (e.g., by conveying the information to a user equipment). A base station (BS) may determine assistance information for a neighboring cell and convey it to a user equipment (UE). A UE may receive assistance information for a neighboring cell and use that information for performing interference cancellation or suppression on received signals. The UE may receive the assistance information from a serving cell or a non-serving cell. The assistance information may be valid for a particular transmission instance, for a known period of time, or until updated by a BS.
Abstract:
A method of wireless communication is presented. The method includes determining, for each resource element group of a resource block pair, whether an interfering control channel is present on the resource block pair. The determination may be based on whether estimated power of the resource element groups varies among two or more resource element groups. The method also includes estimating the interference on the resource block pair based on the determination.
Abstract:
Parameters associated with an interfering downlink transmission may be determined at the UE or may be signaled to the UE from an eNodeB. The parameters may be actual parameters or hypothetical parameters. Based on the parameters, the UE may determine a metric that reflects an amount of interference cancelled from the interfering data channel transmission. The UE determines a quasi-clean channel state information and/or interference efficiency based on the parameters. The UE may transmit the quasi-clean CSI and/or the interference efficiency to the eNodeB.
Abstract:
Monostatic radar with progressive length transmission may be used with half-duplex systems or with full-duplex systems to reduce self-interference. The system transmits a first signal for a first duration and receives a first reflection of the first signal from a first object during a second duration. The system transmits a second signal for a third duration longer than the first duration and receives a second reflection of the second signal from a second object during a fourth duration. The system calculates a position of the first object and the second object based on the first reflection and the second reflection. The first signal, first duration, and second duration are configured to detect reflections from objects within a first distance of the system. The second signal, third duration, and fourth duration are configured to detect reflections from objects between the first distance and a second distance from the system.
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.