Channel availability check optimization may be provided. A plurality of Pulse Repetition Intervals (PRIs) may be determined for a respective plurality of bursts on a respective plurality of frequencies. A list of at least a portion of the plurality of frequencies may be generated. The list may include a plurality of bias factors respectively indicating a probability that each of the respective plurality of bursts was a radar burst based on the respective plurality of PRIs. An Access Point (AP) may perform a plurality of preemptive Channel Availability Checks (CACs) on each of the respective plurality of frequencies on the list in order of highest probability to lowest probability based on the plurality of bias factors.

An information handling system executing an intelligent throughput performance analysis and issue detection system may comprise a network interface device to establish a wireless link with a wireless network and a processor to execute a neural network trained to predict wireless link throughput values based on controlled connectivity testing metrics gathered in a controlled laboratory from tested information handling systems. The processor may gather measured throughput of the wireless link and operational connectivity metrics for the information handling system that describe antenna positional information, antenna adaptation controller parameters, signal strength measurements, and wireless link performance metrics. The neural network may output, based on the gathered operational connectivity metrics a predicted throughput value that differs from the measured throughput by a maximum tolerance. The network interface device may transmit a notification of erroneously predicted throughput, the neural network output, and the operational connectivity metrics to a remote neural network training platform for retraining.

Embodiments of the present invention disclose a link re-establishment method and a device, and relate to the communications field: When detecting that a first downlink beam pair set is invalid, UE sends a first uplink signal to a base station at a moment n by using a first uplink beam pair set through a first uplink channel, where the first uplink signal is used to notify the base station that the first downlink beam pair set is invalid; and the UE detects, at a moment n+k, a first downlink signal sent by the base station by using a second downlink beam pair set through a first downlink channel.

An illustrative battery charging device may identify a battery to be charged, and charge the identified battery using charge settings that are optimized for the identified battery. In some embodiments, the battery charging device may determine the optimized settings based on monitoring charging performance and discharge activities of the battery over time. The battery charging device may exchange data with a battery management service device, such as by exchanging battery health information, battery settings, and/or other data. The battery charging device may determine charge setting and times to charge a battery that is intended to power an unmanned aerial vehicle to complete a flight path.

An illustrative battery charging device may identify a battery to be charged, and charge the identified battery using charge settings that are optimized for the identified battery. In some embodiments, the battery charging device may determine the optimized settings based on monitoring charging performance and discharge activities of the battery over time. The battery charging device may exchange data with a battery management service device, such as by exchanging battery health information, battery settings, and/or other data. The battery charging device may determine charge setting and times to charge a battery that is intended to power an unmanned aerial vehicle to complete a flight path.

The present disclosure pertains to a base station apparatus configured, via machine learning techniques, to provide enhanced communication for one or more user equipment (UEs). This communication may be exemplarily enhanced by detecting rogue base stations and/or by predicting more optimal performance parameters. Some embodiments of this apparatus may include: creating a machine learning classifier using simulated wireless data, the simulated wireless data having known values; and applying, at a base station router (BSR), the machine learning classifier to actual wireless data. This application may be performed by converting between a wireless protocol used by the actual wireless data and a wired protocol used by the simulated wireless data.

The present disclosure pertains to a base station apparatus configured, via machine learning techniques, to provide enhanced communication for one or more user equipment (UEs). This communication may be exemplarily enhanced by detecting rogue base stations and/or by predicting more optimal performance parameters. Some embodiments of this apparatus may include: creating a machine learning classifier using simulated wireless data, the simulated wireless data having known values; and applying, at a base station router (BSR), the machine learning classifier to actual wireless data. This application may be performed by converting between a wireless protocol used by the actual wireless data and a wired protocol used by the simulated wireless data.

According to one aspect, the disclosed subject matter describes herein a method that includes navigating, by a plurality of unmanned aerial vehicles (UAVs), a service coverage area associated with a wireless communications network, wherein the service coverage area includes a plurality of wireless access points and assigning, by at least one of the UAVs, wireless access points to one or more of the UAVs for wireless traffic testing. The method further includes executing, for each of the wireless access points, a wireless traffic test including test traffic data communicated between at least one of the UAVs and a tested wireless access point and determining, for each of the wireless access points to be tested, performance metric information associated with traffic data being wirelessly communicated between the tested wireless access point and the at least one of the UAVs.

According to one aspect, the disclosed subject matter describes herein a method that includes navigating, by a plurality of unmanned aerial vehicles (UAVs), a service coverage area associated with a wireless communications network, wherein the service coverage area includes a plurality of wireless access points and assigning, by at least one of the UAVs, wireless access points to one or more of the UAVs for wireless traffic testing. The method further includes executing, for each of the wireless access points, a wireless traffic test including test traffic data communicated between at least one of the UAVs and a tested wireless access point and determining, for each of the wireless access points to be tested, performance metric information associated with traffic data being wirelessly communicated between the tested wireless access point and the at least one of the UAVs.

A method and apparatus for service quality monitoring in a wireless network is provided. The network has both wired and wireless connections, and has at least one access point capable of providing wireless connectivity to client devices. Wireless connectivity is provided to client devices from the access point with at least one client-serving radio transceiver. Radio test signals are generated for network testing from a testing transceiver at the access point.