REAL-TIME RADIO ACCESS NETWORK ANALYTICS

    公开(公告)号:US20220377577A1

    公开(公告)日:2022-11-24

    申请号:US17398719

    申请日:2021-08-10

    Abstract: Described are examples for providing radio access network (RAN) analytics for a virtualized base station. An analytics engine includes a memory storing one or more parameters or instructions for operating the virtualized RAN and at least one processor coupled to the memory. The analytics engine is configured to perform multiple protocol layers of RAN processing for at least one cell at the virtualized base station. The analytics engine is configured to determine a time series of real-time metrics at two or more layers of the multiple protocol layers for the at least one cell or a user equipment (UE) connected to the at least one cell. The analytics engine is configured to correlate a time series for each of the two or more layers to detect a network condition. The analytics engine is configured to modify a configuration of the at least one cell based on the detected network condition.

    GRAPH PARTITIONING FOR MASSIVE SCALE GRAPHS
    24.
    发明申请

    公开(公告)号:US20190385347A1

    公开(公告)日:2019-12-19

    申请号:US16228754

    申请日:2018-12-20

    Abstract: Graph partitioning for massive scale graphs is described, such as for graphs having vertices representing people and edges representing connections between people in a social networking system; or for graphs where the vertices represent other items and the edges represent relationships between the items. In various embodiments a graph data allocator receives a graph vertex and its edges and allocates the vertex to one of a plurality of clusters each associated with one or more computing devices. In various embodiments the allocation is made by optimizing an objective function which takes into account both a cost of edges between clusters and a cost related to sizes of the clusters. In some examples the cost related to sizes of the clusters comprises a convex function applied to each of the cluster sizes. In examples, computations on the graph data are carried out with reduced runtimes and communications cost.

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