Viavi Solutions Inc.

Kexuan Sun

Wei Li

Li-Ke Huang

Hua Luo

Howard John Thomas

Christopher Michael Murphy

Takai Eddine Kennouche

Agustin Pozuelo

Baruch Friedman

A device may receive real mobile radio data identifying measurements of radio transmissions of base stations and user devices of a mobile radio environment in a geographical area, and may receive network topology data associated with the geographical area. The device may utilize, based on the network topology data, a machine learning feature extraction approach to generate a representation of invariant aspects of spatiotemporal predictable components of the real mobile radio data, and may generate, based on the representation of invariant aspects, stochastic data that includes a probability that a radio signal will be obstructed. The device may utilize the stochastic data to identify a realistic discoverable spatiotemporal signature, and may train or evaluate a system to manage performance of a mobile radio network based on the realistic discoverable spatiotemporal signature.

Utilizing invariant shadow fading data for training a machine learning model

A device may receive mobile radio data identifying utilization of a mobile radio network that includes base stations and user devices in a geographical area. The device may process the mobile radio data, with a machine learning feature extraction model, to generate a behavioral representation, that is probabilistic in nature, of invariant aspects of spatiotemporal utilization of the mobile radio network. The device may generate one or more instances of the spatiotemporal utilization of the mobile radio network that reflects the probabilistic nature of a spatiotemporal predictable component of the behavioral representation. The device may utilize the one or more instances of the spatiotemporal utilization of the mobile radio network as a dataset for training or evaluating a system to manage performance of the mobile radio network.

Utilizing invariant user behavior data for training a machine learning model

A testing device may receive, via a receiving port of a radio frequency (RF) frontend of the testing device, a downlink pilot signal, and may determine a phase associated with the downlink pilot signal. The testing device may transmit, via a transmitting port of the RF frontend of the testing device, an uplink pilot signal. The testing device may receive, after transmitting the uplink pilot signal, the uplink pilot signal via the receiving port of the RF frontend of the testing device. The testing device may determine, after receiving the uplink pilot signal, a phase associated with the uplink pilot signal. The testing device may adjust, based on a phase difference between the phase of the downlink pilot signal and the phase of the uplink pilot signal, one or more transmission settings of the testing device.

Calibrating a testing device that tests base stations with massive MIMO antenna systems

A device determines a scrambling sequence for a control channel, wherein the scrambling sequence includes a first set of bit string values for a set of downlink control information (DCI) candidates associated with the control channel, and wherein the scrambling sequence is determined based on an identifier that is exclusive to the device. The device determines, based on the scrambling sequence, a correlation sequence that includes a second set of bit string values. The device identifies, based on the correlation sequence, a subset of DCI candidates of the set of DCI candidates. The device selects at least a portion of the subset of DCI candidates. The device performs one or more actions based on one or more selected DCI candidates.

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Stevenage, United Kingdom

Kexuan Sun