INTELLIGENT SCHEDULING OF A WIRELESS NETWORK FOR A BACKHAUL

    公开(公告)号:US20200077425A1

    公开(公告)日:2020-03-05

    申请号:US16115506

    申请日:2018-08-28

    Abstract: Systems, methods, and computer-readable media for coordinating scheduling between a wireless scheduler and a backhaul scheduler to reduce uplink data transmission latency. In some examples, an uplink data prediction for uplink data to transmit over a wireless connection of a wireless network is identified by a wireless scheduler of a wireless network. The uplink data prediction can be provided to a backhaul scheduler of a backhaul of the wireless network. The wireless scheduler can be coordinated with the backhaul scheduler by the backhaul scheduler to coordinate transmission of the uplink data through the backhaul based on the uplink data prediction. The uplink data can then be received at the backhaul for transmission through the backhaul based on the uplink data prediction.

    Dynamic fractional frequency reuse

    公开(公告)号:US10798585B2

    公开(公告)日:2020-10-06

    申请号:US16287207

    申请日:2019-02-27

    Abstract: A distributed controller of local radio resources implements a hybrid FFR system. The distributed controller provides user channel information to a central controller of regional radio resources. The distributed controller also obtains an FFR plan from the central controller. The FFR plan designates for an access point associated with the distributed controller, a central frequency band, an edge frequency band, and a distribution of user devices connected to the access point. The distribution indicates whether each user device communicates with the access point via the central frequency band or via the edge frequency band. The distributed controller provisions the access pint to connect to the user devices according to the FFR plan, and adjusts the distribution of user devices locally based on a change in the user channel information.

    Learning-based service migration in mobile edge computing

    公开(公告)号:US11410046B2

    公开(公告)日:2022-08-09

    申请号:US17474191

    申请日:2021-09-14

    Abstract: Learning-based service migration in mobile edge computing may be provided. First, a service migration policy may be created for a network that includes a plurality of edge clouds configured to provide a service to users. Next, a movement of a user receiving the service from a source edge cloud may be detected. The source edge cloud may be associated with a first area and the detected movement may be from the first area to a second area. Then, the service migration policy may be applied to determine whether to migrate the service for the user from the source edge cloud. In response to determining to migrate the service, a target edge cloud may be identified and the service for the user may be migrated from the source edge cloud to the target edge cloud. The service migration policy may then be updated based on a success of the migration.

    DYNAMIC FRACTIONAL FREQUENCY REUSE
    5.
    发明申请

    公开(公告)号:US20200275282A1

    公开(公告)日:2020-08-27

    申请号:US16287207

    申请日:2019-02-27

    Abstract: A distributed controller of local radio resources implements a hybrid FFR system. The distributed controller provides user channel information to a central controller of regional radio resources. The distributed controller also obtains an FFR plan from the central controller. The FFR plan designates for an access point associated with the distributed controller, a central frequency band, an edge frequency band, and a distribution of user devices connected to the access point. The distribution indicates whether each user device communicates with the access point via the central frequency band or via the edge frequency band. The distributed controller provisions the access pint to connect to the user devices according to the FFR plan, and adjusts the distribution of user devices locally based on a change in the user channel information.

    Predictive scheduler
    6.
    发明授权

    公开(公告)号:US10708195B2

    公开(公告)日:2020-07-07

    申请号:US16141517

    申请日:2018-09-25

    Abstract: Predictive scheduling may be provided. First, a first device may identify when a service flow is expected to become active. The first device may estimate an initial traffic profile in response to identifying when the service flow is expected to become active. The first device may then grant allocation based on the initial traffic profile of the service flow. Next, the first device may collect feedback to later update the traffic profile estimate. The first device may then update the traffic profile estimate.

    Learning-based service migration in mobile edge computing

    公开(公告)号:US11132608B2

    公开(公告)日:2021-09-28

    申请号:US16375315

    申请日:2019-04-04

    Abstract: Learning-based service migration in mobile edge computing may be provided. First, a service migration policy may be created for a network that includes a plurality of edge clouds configured to provide a service to users. Next, a movement of a user receiving the service from a source edge cloud may be detected. The source edge cloud may be associated with a first area and the detected movement may be from the first area to a second area. Then, the service migration policy may be applied to determine whether to migrate the service for the user from the source edge cloud. In response to determining to migrate the service, a target edge cloud may be identified and the service for the user may be migrated from the source edge cloud to the target edge cloud. The service migration policy may then be updated based on a success of the migration.

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