NETWORK ASSISTED INTERFERENCE CANCELLATION/SUPPRESSION FOR MULTIPLE SERVICES
    231.
    发明申请
    NETWORK ASSISTED INTERFERENCE CANCELLATION/SUPPRESSION FOR MULTIPLE SERVICES 有权
    网络帮助干扰消除/抑制多个服务

    公开(公告)号:US20140301268A1

    公开(公告)日:2014-10-09

    申请号:US14244739

    申请日:2014-04-03

    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 translation: 本公开的某些方面涉及用于多个服务的网络辅助干扰消除(IC)和干扰抑制(IS)的方法和装置。 根据方面,用户设备(UE)可以确定关于用于在一个或多个相邻小区中发送潜在干扰信号的一种或多种类型的通信服务的系统参数的信息,其中确定的信息的类型取决于通信服务的类型 。 UE可以使用确定的信息来执行干扰管理以消除或抑制由潜在干扰信号引起的干扰。

    TWO-STAGE FREQUENCY DOMAIN MACHINE LEARNING-BASED CHANNEL STATE FEEDBACK

    公开(公告)号:US20250119224A1

    公开(公告)日:2025-04-10

    申请号:US18484269

    申请日:2023-10-10

    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.

    INDICATING CAUSES FOR LIFE CYCLE MANAGEMENT OPERATIONS

    公开(公告)号:US20250048131A1

    公开(公告)日:2025-02-06

    申请号:US18362759

    申请日:2023-07-31

    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.

    DOWNLINK-BASED AI/ML POSITIONING FUNCTIONALITY AND MODEL IDENTIFICATION

    公开(公告)号:US20240414500A1

    公开(公告)日:2024-12-12

    申请号:US18331014

    申请日:2023-06-07

    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.

    UPLINK-BASED ARTIFICIAL INTELLIGENCE / MACHINE LEARNING (AI/ML) POSITIONING FUNCTIONALITY AND MODEL IDENTIFICATION

    公开(公告)号:US20240406927A1

    公开(公告)日:2024-12-05

    申请号:US18329273

    申请日:2023-06-05

    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).

    FUNCTIONALITY BASED TWO-SIDED MACHINE LEARNING OPERATIONS

    公开(公告)号:US20240276241A1

    公开(公告)日:2024-08-15

    申请号:US18408060

    申请日:2024-01-09

    CPC classification number: H04W24/02 H04L41/16

    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.

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