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    • 4. 发明申请
    • CONFIGURATION OF POWER SAVING GROUPS
    • WO2019238227A1
    • 2019-12-19
    • PCT/EP2018/065709
    • 2018-06-13
    • NOKIA TECHNOLOGIES OY
    • MWANJE, StephenALI-TOLPPA, Janne Tapio
    • H04W52/02
    • There are provided measures for configuration of power saving groups. Such measures, for determining, in a network including a plurality of radio cells, at least one power saving group comprising at least two radio cells of said plurality of radio cells, exemplarily comprise retrieving neighboring data including a plurality of entries corresponding to a plurality of combinations of respective two radio cells of said plurality of radio cells, wherein each of said plurality of entries represents overlapping amount information in relation to said respective two radio cells, identifying radio cells of said plurality of radio cells as power saving group reference cells based on said neighboring data, identifying radio cells of said plurality of radio cells as power saving group helping cells respectively for at least one identified power saving group reference cell based on said neighboring data, and assigning each of said identified power saving group helping cells to one of said identified power saving group reference cells based on said neighboring data, wherein each of said at least one power saving group comprises one of said identified power saving group reference cells and at least one identified power saving group helping cell assigned to said one of said identified power saving group reference cells.
    • 6. 发明申请
    • SAMPLING USER EQUIPMENTS FOR FEDERATED LEARNING MODEL COLLECTION
    • WO2022089751A1
    • 2022-05-05
    • PCT/EP2020/080435
    • 2020-10-29
    • NOKIA TECHNOLOGIES OY
    • BUTT, Muhammad MajidMWANJE, StephenSYED MUHAMMAD, Fahad
    • G06N20/00
    • First user equipments are detected out of a plurality of user equipments of a cellular communication system (S201). The user equipments respectively correspond to a distributed node of a federated machine-learning concept and respectively generate a partial machine-learning model, wherein partial machine-learning models generated by the plurality of user equipments are to be used to update a global machine-learning model at the network side of the cellular communication system. The first user equipments are user equipments comprising ready partial machine-learning models. Out of the first user equipments, second user equipments are selected at least based on a time information associated with the first user equipments (S203), the ready partial machine-learning models respectively generated by the second user equipments are acquired (S205), the global machine-learning model is updated using the ready partial machine-learning models acquired (S207), and convergence of the global machine-learning model updated by the ready partial machine-learning models acquired is determined (S209). In case convergence of the global machine-learning model is not determined, a process comprising the detecting (S201), selecting (S203), acquiring (S205), updating (S207) and determining (S209) is repeated.