CAPACITY OPTIMIZATION IN A COMMUNICATION NETWORK
    56.
    发明申请
    CAPACITY OPTIMIZATION IN A COMMUNICATION NETWORK 审中-公开
    通信网络中的容量优化

    公开(公告)号:US20160226739A1

    公开(公告)日:2016-08-04

    申请号:US14738498

    申请日:2015-06-12

    Abstract: Systems and methods presented herein provide for optimizing or otherwise improving capacity in a communication network. In one embodiment, a method is operable in a network hub that is communicatively coupled to UEs via a plurality of communication links. Each communication link has a different data rate capacity and each UE comprises a profile that indicates its data rate capabilities (e.g., modulation schemes and the like). The method includes detecting communications of the UEs, determining the data rate capabilities of the UEs from their profiles, and determining how many different data rate capacities the network hub supports over the communication links. The method also includes grouping the UEs according to their data rate capabilities, generating a common profile for each UE group, and assigning the communication links to the UE groups based on their common profiles.

    Abstract translation: 本文提出的系统和方法提供优化或改善通信网络中的容量。 在一个实施例中,一种方法可在通过多个通信链路与UE通信耦合的网络集线器中操作。 每个通信链路具有不同的数据速率容量,并且每个UE包括指示其数据速率能力的简档(例如,调制方案等)。 该方法包括检测UE的通信,从他们的简档确定UE的数据速率能力,以及确定网络集线器在通信链路上支持多少个不同的数据速率容量。 该方法还包括根据其数据速率能力对UE进行分组,为每个UE组生成公共简档,并且基于它们的共同简档将通信链路分配给UE组。

    Analysis of captured signals to measure nonlinear distortion
    57.
    发明授权
    Analysis of captured signals to measure nonlinear distortion 有权
    捕获信号的分析以测量非线性失真

    公开(公告)号:US09225387B2

    公开(公告)日:2015-12-29

    申请号:US14184619

    申请日:2014-02-19

    CPC classification number: H04B3/46 H04W24/08

    Abstract: A method to test a signal path with vacant bandwidth by sending a test signal twice and processing two resulting nonlinear distortion signals captured in the vacant bands to determine presence of nonlinear distortion. If the signals correlate, the energy in the vacant bands is nonlinear distortion. If the test signal is sent followed by an inverse (in time-domain) of itself, and a resulting correlation peak is negative, the nonlinear distortion is determined to have been created by odd-order nonlinear distortion.

    Abstract translation: 通过发送测试信号两次来测试具有空闲带宽的信号路径的方法,并处理在空闲频带中捕获的两个产生的非线性失真信号,以确定非线性失真的存在。 如果信号相关,则空位带中的能量是非线性失真。 如果发送测试信号后跟本身的反向(在时域中),并且所得到的相关峰值为负,则非线性失真被确定为由奇次非线性失真产生。

    SIGNALING WITH NOISE CANCELLATION USING ECHOES
    58.
    发明申请
    SIGNALING WITH NOISE CANCELLATION USING ECHOES 有权
    使用ECHOES进行噪声消除的信号

    公开(公告)号:US20150139349A1

    公开(公告)日:2015-05-21

    申请号:US14083564

    申请日:2013-11-19

    Inventor: Belal Hamzeh

    CPC classification number: H04L1/20 H04B1/62 H04L25/028

    Abstract: Signal transport with noise cancellation is contemplated. The noise cancellation may be facilitated with a transmitter configured to induce echoes in a signal desired for transport in order to facilitate subsequently retrieving signal components associated with noise influenced portions of the transported signal from non-noise influence portions of the transported signal.

    Abstract translation: 考虑了具有噪声消除的信号传输。 噪声消除可以通过发射机被配置为在期望传输的信号中引起回波以促进随后从传送信号的非噪声影响部分检索与被传输信号的噪声影响部分相关联的信号分量的便利。

    Methods for network maintenance
    59.
    发明授权

    公开(公告)号:US12294480B2

    公开(公告)日:2025-05-06

    申请号:US16416016

    申请日:2019-05-17

    Abstract: Methods, systems, and devices for network maintenance are described. A controller may receive an indication that the performance of a network device has decreased. The indication may include case identification data associated with the network device. The controller may request proactive network maintenance (PNM) data associated with the network device based on receiving the case identification data. The controller may transmit the PNM data to a machine learning (ML) engine. The ML engine may transmit, to the controller, an indication of a predetermined operation predicted to improve the performance of the network device. The controller may transmit the predetermined operation to a client device based on receiving the indication of the predetermined operation. The ML engine may receive feedback indicating whether an execution of the predetermined operation improved the performance of the network device. The ML engine may a training data set based on the feedback.

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