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EP-4147478-B1 - MEASUREMENT CONFIGURATION FOR LOCAL AREA MACHINE LEARNING RADIO RESOURCE MANAGEMENT

EP4147478B1EP 4147478 B1EP4147478 B1EP 4147478B1EP-4147478-B1

Inventors

  • SÄILY, Mikko
  • VEIJALAINEN, Teemu Mikael

Dates

Publication Date
20260506
Application Date
20210421

Claims (6)

  1. A method of communications, comprising: receiving, by a user equipment (131, 132, 133, 135), a validity area and a measurement group of at least one machine learning, ML, model from a network node (134), wherein the validity area comprises one or more cells in which the at least one ML model performs inference; performing, by the user equipment, measurements based at least on the measurement group, when the user equipment is connected to a cell included in the validity area, wherein the measurement group defines cells where measurements are required by the at least one ML model, wherein the measurement group comprises at least one cell which is not comprised within the validity area; transmitting, by the user equipment, the measurements to the network node; receiving, by the user equipment, the at least one ML model, wherein the received at least one ML model has been trained at the network node based at least on the measurements transmitted to the network node; and performing, by the user equipment, one or more radio resource management operations based on the trained ML model.
  2. A method of communications, comprising: defining, by a network node (134), a validity area and a measurement group for at least a machine learning, ML, model for one or more user equipments (131, 132, 133, 135), wherein the validity area is the area in which the at least one ML model performs inference; transmitting, by the network node, the validity area and the measurement group of the at least one ML model to a user equipment of the one or more user equipments; receiving, by the network node, measurements from the user equipment, the measurements performed at the user equipment based at least on the measurement group, when the user equipment is connected to a cell included in the validity area, wherein the measurement group defines cells where measurements are required by the at least one ML model, wherein the measurement group comprises at least one cell which is not comprised within the validity area; training the at least one ML model based at least on the received measurements; transmitting, by the network node, the at least one trained ML model to the user equipment; and performing, by the network node, one or more radio resource management operations based at least on the trained ML model.
  3. The method of any of claims 1-2, wherein at least one of the validity area or the measurement group comprises at least one of: a cell (136), a list of cells, a cell group ID, or cell group IDs
  4. A user equipment (131, 132, 133, 135), comprising: means for receiving a validity area and a measurement group of at least one machine learning, ML, model from a network node (134), wherein the validity area comprises one or more cells in which the at least one ML model performs inference; means for performing measurements based at least on the measurement group, when the apparatus is connected to a cell included in the validity area, wherein the measurement group defines cells where measurements are required by the at least one ML model, wherein the measurement group comprises at least one cell which is not comprised within the validity area; means for transmitting, by the user equipment, the measurements to the network node; means for receiving, by the user equipment, the at least one ML model, wherein the received at least one ML model has been trained at the network node based at least on the measurements transmitted to the network node; and means for performing, by the user equipment, one or more radio resource management operations based on at least one of the measurements, the ML model, or the trained ML model.
  5. A network node (134), comprising: means for defining a validity area and a measurement group for at least a machine learning, ML, model for one or more user equipments (131, 132, 133, 135), wherein the validity area is the area in which the at least one ML model performs inference; means for transmitting the validity area and the measurement group of the at least one ML model to a user equipment of the one or more user equipments; means for receiving measurements from the user equipment, the measurements performed at the user equipment based at least on the measurement group, when the user equipment is connected to a cell included in the validity area, wherein the measurement group defines cells where measurements are required by the at least one ML model, wherein the measurement group comprises at least one cell which is not comprised within the validity area; means for training the at least one ML model based at least on the received measurements; means for transmitting the at least one trained ML model to the user equipment; and means for performing one or more radio resource management operations based at least on the received measurements, the ML model, or the trained ML model.
  6. The user equipment of claim 4 or the network node of claim 5, further, comprising: means for performing the method according to claim 3.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS This application claims priority to United States provisional application no. 63/020,466, filed May 5, 2020, entitled "MEASUREMENT CONFIGURATION FOR LOCAL AREA MACHINE LEARNING RADIO RESOURCE MANAGEMENT". TECHNICAL FIELD This description relates to wireless communications, and in particular, machine learning based radio resource management. BACKGROUND A communication system may be a facility that enables communication between two or more nodes or devices, such as fixed or mobile communication devices. Signals can be carried on wired or wireless carriers. An example of a cellular communication system is an architecture that is being standardized by the 3rd Generation Partnership Project (3GPP). A recent development in this field is often referred to as the long-term evolution (LTE) of the Universal Mobile Telecommunications System (UMTS) radio-access technology. E-UTRA (evolved UMTS Terrestrial Radio Access) is the air interface of 3GPP's Long Term Evolution (LTE) upgrade path for mobile networks. In LTE, base stations or access points (APs), which are referred to as enhanced Node AP or Evolved Node B (eNBs), provide wireless access within a coverage area or cell. In LTE, mobile devices, or mobile stations are referred to as user equipments (UE). LTE has included a number of improvements or developments. 5G New Radio (NR) development is part of a continued mobile broadband evolution process to meet the requirements of 5G, similar to earlier evolution of 3G & 4G wireless networks. In addition, 5G is also targeted at the new emerging use cases in addition to mobile broadband. A goal of 5G is to provide significant improvement in wireless performance, which may include new levels of data rate, latency, reliability, and security. 5G NR may also scale to efficiently connect the massive Internet of Things (IoT), and may offer new types of mission-critical services. Ultra-reliable and low-latency communications (URLLC) devices may require high reliability and very low latency. US10039016 B1 discloses a method for obtaining reference signal measurements over a structured interface to support RF optimization via machine learning as known in the art. WO2019/172813 A1 discloses a method performed by a wireless communication device for managing communication in a wireless communications network as known in the art. WO2020/080989 A1 discloses a method for handling of machine learning as known in the art. SUMMARY Various example implementations (or embodiments) are described and/or illustrated. The details of one or more examples of embodiments are set forth in the accompanying drawings and the description below. Other features will be apparent from the description and drawings, and from the claims. The invention is defined by the independent claims. Optional features are defined in the dependent claims. BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 is a block diagram of a wireless network according to an example embodiment.FIG. 2 is a block diagram illustrating machine learning area measurement configuration for machine learning model based radio resource management, according to an example embodiment.FIG. 3 illustrates a validity area and measurement group defined with a measurement object, according to an example embodiment.FIG. 4 illustrates a validity area and measurement group defined with a measurement ID, according to an example embodiment.FIG. 5 illustrates measurement object groups for inter-frequency and inter-RAT configuration, according to an example embodiment.FIG. 6 is a flow chart illustrating a mechanism for machine learning (ML) model based radio resource management, according to an example embodiment.FIG. 7 is a flow chart illustrating another mechanism for machine learning based radio resource management, according to an additional example embodiment.FIG. 8 is a block diagram of a node or wireless station (e.g., base station/access point or mobile station/user device/UE), according to an example embodiment. DETAILED DESCRIPTION FIG. 1 is a block diagram of a wireless network 130 according to an example embodiment. In the wireless network 130 of FIG. 1, user devices (UDs) 131, 132, 133 and 135, which may also be referred to as mobile stations (MSs) or user equipment (UEs), may be connected (and in communication) with a base station (BS) 134, which may also be referred to as an access point (AP), an enhanced Node B (eNB), a next-generation Node B (gNB) or a network node. At least part of the functionalities of an access point (AP), base station (BS), (e)Node B (eNB), or gNB may also be carried out by any node, server or host which may be operably coupled to a transceiver, such as a remote radio head. BS (or AP) 134 provides wireless coverage within a cell 136, including to user devices 131, 132, 133 and 135. Although only four user devices are shown as being connected or attached to BS 134, any number of user devices may be provided. BS 134 is also connected to a core n