Adaptive resource allocation for media streams over wireless

    公开(公告)号:US11611970B2

    公开(公告)日:2023-03-21

    申请号:US17195103

    申请日:2021-03-08

    Abstract: A method is provided in a wireless access point in a wireless communications network. The method includes obtaining information characterizing a first wireless stream and the second wireless stream transmitted or received by the wireless access point. The information includes at least a wireless channel quality for each of the first wireless stream and the second wireless stream. The method further includes allocating transmission resources to the first wireless stream and the second wireless stream based on the obtained information. In response to a change in quality of the first wireless stream, the method further includes revising the allocation of transmission resources for the first wireless stream based on at least one of a target bit-rate and a target level of smoothness.

    Wireless station ranging using channel state

    公开(公告)号:US11265842B2

    公开(公告)日:2022-03-01

    申请号:US16812427

    申请日:2020-03-09

    Abstract: Techniques for determining a range between a wireless station (STA) and a wireless access point (AP) using channel state information are described. An AP determines channel state information corresponding to an STA. The AP determines, based on the channel state information, one or more fine timing measurement (FTM) parameters. A plurality of FTM messages are transmitted between the AP and the STA, based on the one or more FTM parameters. The STA is configured to determine an estimated range to the AP based on the plurality of FTM messages.

    Training distributed machine learning with selective data transfers

    公开(公告)号:US11144616B2

    公开(公告)日:2021-10-12

    申请号:US15439072

    申请日:2017-02-22

    Abstract: Presented herein are techniques for training a central/global machine learning model in a distributed machine learning system. In the data sampling techniques, a subset of the data obtained at the local sites is intelligently selected for transfer to the central site for use in training the central machine learning model. In the model merging techniques, distributed local training occurs in each local site and copies of the local machine learning models are sent to the central site for aggregation of learning by merging of the models. As a result, in accordance with the examples presented herein, a central machine learning model can be trained based on various representations/transformations of data seen at the local machine learning models, including sampled selections of data-label pairs, intermediate representation of training errors, or synthetic data-label pairs generated by models trained at various local sites.

    ADAPTIVE RESOURCE ALLOCATION FOR MEDIA STREAMS OVER WIRELESS

    公开(公告)号:US20210195607A1

    公开(公告)日:2021-06-24

    申请号:US17195103

    申请日:2021-03-08

    Abstract: A method is provided in a wireless access point in a wireless communications network. The method includes obtaining information characterizing a first wireless stream and the second wireless stream transmitted or received by the wireless access point. The information includes at least a wireless channel quality for each of the first wireless stream and the second wireless stream. The method further includes allocating transmission resources to the first wireless stream and the second wireless stream based on the obtained information. In response to a change in quality of the first wireless stream, the method further includes revising the allocation of transmission resources for the first wireless stream based on at least one of a target bit-rate and a target level of smoothness.

    Adaptive resource allocation for media streams over wireless

    公开(公告)号:US10966216B2

    公开(公告)日:2021-03-30

    申请号:US16555501

    申请日:2019-08-29

    Abstract: A method is provided in a wireless access point in a wireless communications network. The method includes obtaining information characterizing a first wireless stream and the second wireless stream transmitted or received by the wireless access point. The information includes at least a wireless channel quality for each of the first wireless stream and the second wireless stream. The method further includes allocating transmission resources to the first wireless stream and the second wireless stream based on the obtained information. In response to a change in quality of the first wireless stream, the method further includes revising the allocation of transmission resources for the first wireless stream based on at least one of a target bit-rate and a target level of smoothness.

    Multi-class orthogonal frequency-division multiple access (OFDMA) scheduling

    公开(公告)号:US10952245B1

    公开(公告)日:2021-03-16

    申请号:US16655005

    申请日:2019-10-16

    Abstract: Multi-Class Orthogonal Frequency-Division Multiple Access (OFDMA) scheduling may be provided. A plurality of network devices may be assigned a transmission window for channel access. The plurality of network devices may include a first number of client devices and a second number of client devices. A first signal may be sent to the first number of network devices. The first signal may enable the first number of network devices to the channel access in a first portion of the transmission window. An amount of data to be exchanged by each of the second number of network devices may be determined. Based on the determined amount of data to be exchanged, a schedule may be determined for the channel access for each of the second number of network devices in a second portion of the transmission window. A second signal may be sent to the second number of network devices based on the determined schedule. The second signal may enable the second number of network devices to the channel access in the second portion of the transmission window.

    LEARNING-BASED WIRELESS TRANSMISSION PARAMETER ADAPTATION BASED ON CLIENT ACTIVITY DETECTION

    公开(公告)号:US20200287639A1

    公开(公告)日:2020-09-10

    申请号:US16292998

    申请日:2019-03-05

    Abstract: An access point (AP) is configured to transmit packets to a client device over a communication channel. The AP determines a motion indictor indicative of motion of the client device based on a sequence of channel state information measurements, and measures a signal-to-noise ratio (SNR). The AP selects a transmission parameter among candidate transmission parameters using a learning-based algorithm based on observation parameters including the motion indicator, the SNR, and a device identifier for the client device. The AP employs the transmission parameter to transmit packets to the client device, and measures a transmission performance associated with the transmission parameter based on the transmitted packets. The AP updates the learning-based algorithm based on the observation parameters and the transmission performance for a next pass through the selecting, the employing, and the measuring.

    Adaptive localization and incremental deployment of infrastructure with crowd-sourced feedback

    公开(公告)号:US10145962B1

    公开(公告)日:2018-12-04

    申请号:US15726553

    申请日:2017-10-06

    Abstract: A methodology includes receiving from a first mobile device a first estimated location of the first mobile device and a first estimated error associated with the first estimated location, the first estimated location being based on first coarse data from a first wireless access point location determination system fused with inertial measurement unit (IMU) data from the first mobile device, receiving from a second mobile device a second estimated location of the second mobile device and a second estimated error associated with the second estimated location, the second estimated location being based on second coarse data from the first wireless access point location determination system fused with IMU data from the second mobile device, and based on the first estimated error and the second estimated error, determining a recommended change to a deployment of a wireless access point associated with the first wireless access point location determination system.

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