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    • 3. 发明申请
    • PROACTIVE LINK BLOCKAGE AVOIDANCE FOR RELIABLE MMWAVE COMMUNICATION
    • WO2022083860A1
    • 2022-04-28
    • PCT/EP2020/079682
    • 2020-10-22
    • TELEFONAKTIEBOLAGET LM ERICSSON (PUBL)
    • KALANTARI, AshkanBALAKRISHNAN, Bipin
    • H04B7/06H04B7/08
    • Blockage of a mmWave communication link, due to movement of one or both of the wireless devices, and/or movement of an obstacle, is predicted. An environment is monitored by imaging devices, which may be fixed or mobile, and may be on the wireless devices. Objects in the environment are detected and their motion tracked from the image data. Based on the motion of the wireless device(s) and/or an obstacle, a link blockage event – whereby an obstacle interrupts communications on the link – is predicted, and a start time is estimated. Prior to the start time, directional antenna beams of both wireless devices are directed to a passive reflector, and the mmWave communication link is routed around the obstacle. Passive reflectors may be deployed throughout the environment. They may be moveable in angle and tilt to assist in avoiding link blockage. Beam re-training is performed on a group of directional antenna beam pairs directed towards the passive reflector. The directional antenna beam pairs are ranked by a channel quality metric, such as SINR, and a pair is chosen for use during the blockage (two pairs for duplex links). A different frequency may be used for the blockage avoidance communication link, reducing interference and allowing the same passive reflector to be used by more than one wireless communication link. The wireless communication link is transferred to the re-trained directional antenna beams, directed to a passive reflector, prior to the start of the link blockage event. For the duration of blockage, the wireless devices communicate via the passive reflector, without loss or interruption, which is critical in URLLC use cases.
    • 8. 发明申请
    • LOAD DISTRIBUTION FOR A DISTRIBUTED NEURAL NETWORK
    • WO2020164698A1
    • 2020-08-20
    • PCT/EP2019/053536
    • 2019-02-13
    • TELEFONAKTIEBOLAGET LM ERICSSON (PUBL)
    • BASTANI, SaeedLI, YunBERKEMAN, AndersHENNINGSSON, MariaKALANTARI, Ashkan
    • G06N3/063G06N3/04
    • A method for dynamic load distribution for a distributed neural network is disclosed. The method comprises estimating (103), in a device of the neural network, an energy usage for processing at least one non-processed layer in the device, and estimating (106), in the device of 5 the neural network, an energy usage for transmitting layer output of at least one processed layer to a cloud service of the neural network for processing. The method further comprises comparing (107), in the device of the neural network, the estimated energy usage for processing the at least one non-processed layer in the device with the estimated energy usage for transmitting the layer output of the at least one processed layer to the cloud service. The 10 method furthermore comprises determining (108) to process the at least one non-processed layer in the device when the estimated energy usage for transmitting the layer output of the at least one processed layer to the cloud service is equal or greater than the estimated energy usage for processing the at least one non-processed layer, and determining (110) to transmit the layer output of the at least one processed layer to the cloud service for processing 15 subsequent layers when the estimated energy usage for transmitting the layer output of the at least one processed layer to the cloud service is less than the estimated energy usage for processing the at least one non-processed layer in the device. Corresponding computer program product, apparatus, cloud service assembly, and system are also disclosed.20