Devices and methods enable optimizing a signal of interest based on identifying and analyzing the signal of interest based on radio frequency energy measurements. Signal data is compared with stored data to identify the signal of interest. Signal degradation data is calculated based on noise figure parameters, hardware parameters and environment parameters. The signal of interest is optimized based on the signal degradation data. Terrain data may also be used for optimizing the signal of interest.

The present technology includes calculating the 3-D RF propagation pattern in a space for at least one Wi-Fi access point and displaying a visualization of the RF propagation pattern in augmented reality (AR). The augmented reality view of the space can be created by capturing at least one image of the space and displaying at least one image of the space on a display with the visualization of the Wi-Fi access point RF propagation pattern on the display overlaid at least one image of the space. The disclosed technology further can calculate the RF propagation properties and render a visualization of the RF propagation patterns in a 3D space by utilizing hardware on a user device. The AR display is useful in visualizing, in-person aspects of a Wi-Fi network and coverage, and can be used in troubleshooting, maintenance, and simulations of equipment variations.

In one embodiment, a network assurance service that monitors a network maps time series of values of key performance indicator (KPIs) measured from the network to lists of unique values from the time series. The service sets a target alarm rate for anomaly detection alarms raised by the network assurance service. The service uses an optimization function to identify a set of thresholds for the KPIs. The optimization function is based on: a comparison between the target alarm rate and a fraction of network issues flagged by the service as outliers, KPI thresholds selected based on the lists of unique values from the time series, and a number of thresholds that the KPIs must cross for the service to raise an alarm. The service raises an anomaly detection alarm for the monitored network based on the identified set of thresholds for the KPIs.

Methods may include alerting fixed service microwave operators that their network has been compromised due to interference, before their service is necessarily impacted. In an example, method may include receiving instructions to test fade margin for a wireless communication link associated with a receiver radio, wherein the wireless communication link is passing live traffic; testing the fade margin while the wireless communication link is passing the live traffic; receiving error information, wherein the error information is based on an error correction mechanism for the wireless communication link; based on the error information, determining that a threshold number of errors has been reached; and sending an alert based on the threshold number of errors.

The present disclosure relates to a method (10) for characterizing a wideband RF device-under-test (DUT) by means of a narrowband RF source or a narrowband RF receiver, the method (10) comprising: selecting (11) a bandwidth of the wideband RF DUT to be analyzed; dividing (12) the selected bandwidth into at least two overlapping sub-bands, the respective sub-bands having a frequency range that corresponds to a bandwidth of the narrowband RF source or the narrowband RF receiver; acquiring (13) a response of the wideband RF DUT for each of the at least two overlapping sub-bands by means of at least two narrowband measurements using the narrowband RF source or the narrowband RF receiver; and calculating (14) a continuous amplitude response and a continuous phase response of the wideband RF DUT in a frequency range that corresponds to the combined bandwidth of the at least two overlapping sub-bands, said calculation making use of the overlap of the sub-bands.

The present disclosure relates to a method (10) for characterizing a wideband RF device-under-test (DUT) by means of a narrowband RF source or a narrowband RF receiver, the method (10) comprising: selecting (11) a bandwidth of the wideband RF DUT to be analyzed; dividing (12) the selected bandwidth into at least two overlapping sub-bands, the respective sub-bands having a frequency range that corresponds to a bandwidth of the narrowband RF source or the narrowband RF receiver; acquiring (13) a response of the wideband RF DUT for each of the at least two overlapping sub-bands by means of at least two narrowband measurements using the narrowband RF source or the narrowband RF receiver; and calculating (14) a continuous amplitude response and a continuous phase response of the wideband RF DUT in a frequency range that corresponds to the combined bandwidth of the at least two overlapping sub-bands, said calculation making use of the overlap of the sub-bands.

Exemplary embodiments are disclosed of signal level indicators and antenna assemblies including the same. In an exemplary embodiment, an antenna assembly includes an antenna configured to be operable for receiving signals, a signal level indicator for indicating a strength of signals received by the antenna, and an amplifier coupled for communication with the antenna, the signal level indicator, and a signal output. The amplifier is configured to be operable for amplifying signals received by the antenna.

A computing system receives data indicating signal strength of signals transmitted between a plurality of beacons and a mobile device. The system determines a location of the mobile device within a vehicle coordinate system, wherein the beacons have known locations within the coordinate system, the determination being based in part on the signal strength. The system also determines that the location of the mobile device is proximate to a predefined vehicle element location and instructs issuance of an alert responsive to the proximity

An unmanned aerial vehicle (UAV) is dispatched to automatically fly to a location of an antenna indicated by GPS coordinates, automatically recognize the antenna at that location using automated image analysis and object detection, and then start capturing still or video imagery of the antenna in response to the recognition of the antenna. The UAV may be automatically activated to start looking for debris, obstructions or other objects affecting line-of-sight signal reception by the antenna in response to arriving at the location of the antenna and/or detection of the antenna. The UAV may automatically transmit (e.g., in real time) the still or video aerial imagery of the antenna to a backend system for analysis to determine whether there exists a specific issue (e.g., snow or other debris on the antenna) affecting wireless communication involving the antenna.

An unmanned aerial vehicle (UAV) is dispatched to automatically fly to a location of an antenna indicated by GPS coordinates, automatically recognize the antenna at that location using automated image analysis and object detection, and then start capturing still or video imagery of the antenna in response to the recognition of the antenna. The UAV may be automatically activated to start looking for debris, obstructions or other objects affecting line-of-sight signal reception by the antenna in response to arriving at the location of the antenna and/or detection of the antenna. The UAV may automatically transmit (e.g., in real time) the still or video aerial imagery of the antenna to a backend system for analysis to determine whether there exists a specific issue (e.g., snow or other debris on the antenna) affecting wireless communication involving the antenna.