ACTIVE-ACTIVE TDM PW WITH ASYMMETRY CONTROL
    191.
    发明公开

    公开(公告)号:US20230198647A1

    公开(公告)日:2023-06-22

    申请号:US17552591

    申请日:2021-12-16

    CPC classification number: H04J3/0629 H04L47/283 H04L47/34 H04L45/24

    Abstract: A method for enabling enable use of multiple active paths for TDM traffic over a packet switched network, comprises: receiving at least two copies of a replicated packet including TDM information via at least two paths through the packet switched network, the at least two copies of the replicated packet including at least a first copy of the replicated packet received via a first of the at least two paths, and a second copy of the replicated packet received via a second of the at least two paths; selecting a copy of the replicated packet from among the at least two copies of the replicated packet; inputting the selected copy of the replicated packet to a jitter buffer; discarding unselected ones of the at least two copies of the replicated packet; and outputting the selected copy of the replicated packet from the jitter buffer to a TDM endpoint device.

    DEVICE POSITIONING
    193.
    发明公开
    DEVICE POSITIONING 审中-公开

    公开(公告)号:US20230184879A1

    公开(公告)日:2023-06-15

    申请号:US18089331

    申请日:2022-12-27

    CPC classification number: G01S5/10 G01S5/02216 G01S5/02685 G01S5/0036

    Abstract: An apparatus, method and computer program is described. The method can include receiving a first measurement report from a first communication node of a mobile communication system. The first measurement report can include downlink measurement data generated at a user device in response to a positioning reference signal sent by the first communication node. The method can further include receiving a second measurement report from the first communication node. The second measurement report can include uplink measurement data generated at the first communication node in response to an uplink reference signal sent by the user device. The method can also include determining an integrity of the measurement data based on a comparison of said uplink and downlink measurement data and setting an integrity verification notification in accordance with the determined integrity.

    MULTI-TIER DETERMINISTIC NETWORKING
    196.
    发明公开

    公开(公告)号:US20230164071A1

    公开(公告)日:2023-05-25

    申请号:US18100214

    申请日:2023-01-23

    CPC classification number: H04L45/70 H04L45/08 H04L45/04 H04L43/087 H04L43/0858

    Abstract: Various example embodiments for supporting multi-tier deterministic networking are presented. Various example embodiments for supporting multi-tier deterministic networking may be configured to support provisioning of deterministic flows in multi-tier deterministic networking. Various example embodiments for supporting multi-tier deterministic networking may be configured to support adaptive deterministic routing in multi-tier deterministic networks. Various example embodiments for supporting multi-tier deterministic networking may be configured to support score-based deterministic routing in multi-tier deterministic networks. Various example embodiments for supporting multi-tier deterministic networking may be configured to support adaptive deterministic routing and/or score-based deterministic routing in multi-tier deterministic networks based on analysis of a state representation for path and/or sub-path selection in multi-tier deterministic networks. Various example embodiments for supporting multi-tier deterministic networking may be configured to support hierarchical resource allocation and deallocation in multi-tier deterministic networking, optimal route finding in multi-tier deterministic networking, and so forth.

    Efficient transfer of access context for user equipment among network nodes

    公开(公告)号:US11659453B2

    公开(公告)日:2023-05-23

    申请号:US17183966

    申请日:2021-02-24

    Abstract: The present disclosure relates generally to the field of wireless communications, and in particular to techniques for efficiently transferring, among network nodes, an access context required to initiate data transfer when a user equipment (UE) is in an active or suspended Radio Access Network (RAN) connection state. The techniques disclosed herein involve identifying, based on UE-specific data (such, for example, as mobility information, a traffic profile), one or more relevant network nodes where the UE in the suspended RAN connection state could be located at the time when next data transfer needs to be initiated. After that, the access context is sent from an anchor network node to said one or more relevant network nodes. By sending the access context in this manner, network signalling overhead and storage capacity overhead may be significantly reduced.

    Obstacle detection
    199.
    发明授权

    公开(公告)号:US11645762B2

    公开(公告)日:2023-05-09

    申请号:US17281815

    申请日:2018-10-15

    Inventor: Niroj Pokhrel

    Abstract: An apparatus, method and computer program is described comprising the following: receiving telemetry and/or motion vector data and distance data from a distance sensor of the drone or unmanned aerial vehicle (32); determining a segment of interest dependent on said telemetry data and/or motion vector data; processing said distance data to determine whether an obstacle falls within said segment of interest (34); receiving imaging data; and providing imaging data analysis in the event that an obstacle is determined to fall within said segment of interest (36).

    DEEP REINFORCEMENT LEARNING BASED WIRELESS NETWORK SIMULATOR

    公开(公告)号:US20230135745A1

    公开(公告)日:2023-05-04

    申请号:US17964704

    申请日:2022-10-12

    Abstract: According to an example embodiment, a device is configured to a deep reinforcement learning, DRL, agent to simulate the behaviour of a network component. The DRL agent takes the network state and user traffic as inputs. It generates the next network state and user performances. A training algorithm of the simulator is configured for the DRL agents and it is derived to deal with the property of time-correlation in network components. The simulator uses a training algorithm so that it enables robust inference under a limited number of transitions collected with the real network components and users. It is derived with state augmentation by using an autoencoder architecture. It is also configured by a reward estimation algorithm by using local regression, for example with a Gaussian Process.

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