Systems, methods, and devices enable spectrum management by identifying, classifying, and cataloging signals of interest based on radio frequency 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.

A method of path loss estimation at a user equipment (UE) comprises receiving a downlink cell-specific signal block comprising a synchronization channel and a broadcasting channel demodulation reference signal; receiving control information indicative of a signal transmission power of the downlink cell-specific signal block; and determining an estimated path loss for the UE based at least in part on the signal transmission power of the downlink cell-specific signal block and a received power of the downlink cell-specific signal block filtered using a layer 3 filtering coefficient.

Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for processing communications signals using a machine-learning network are disclosed. In some implementations, pilot and data information are generated for a data signal. The data signal is generated using a modulator for orthogonal frequency-division multiplexing (OFDM) systems. The data signal is transmitted through a communications channel to obtain modified pilot and data information. The modified pilot and data information are processed using a machine-learning network. A prediction corresponding to the data signal transmitted through the communications channel is obtained from the machine-learning network. The prediction is compared to a set of ground truths and updates, based on a corresponding error term, are applied to the machine-learning network.

Systems, methods and apparatus for spectrum data management for a radio frequency (RF) environment are disclosed. An apparatus comprises at least one receiver, an automatic signal detection (ASD) module, and a learning and conflict detection engine. The apparatus is at the edge of a communication network. The at least one receiver processes RF energy received from the RF environment, thereby generating processed data. The ASD module is configured to extract meta data and detect anomaly based on the processed data. The learning and conflict detection engine is configured for conflict recognition and anomaly identification based on the processed data. The apparatus is operable to generate at least one report for the RF environment.

Anomalies at nodes of a wireless network can be detected by receiving performance measurement (PM) data for a plurality of nodes in the wireless telecommunications network, accessing sets of binned data of the PM data for each node of the plurality of nodes, comparing at least one characteristic of the binned data for each node to at least one of a threshold value and the binned data for another node of the plurality of nodes, and determining whether an anomaly is present at each of the plurality of nodes based on a result of the comparison.

In some embodiments, a method obtains raw data from one or more packets received over a wireless channel via an antenna. The raw data comprises raw amplitude values and raw phase values. Each raw amplitude value and raw phase value corresponds to a respective OFDM symbol and subcarrier of a respective packet. The method further comprises processing the raw data according to an interference mitigation process and using the resulting calibrated amplitude values and calibrated phase values to determine weighted phase values. Each weighted phase value corresponds to a respective subcarrier. The method determines a phase variance for the antenna based on comparing the plurality of weighted phase values across the plurality of subcarriers. The method determines whether the wireless channel experiences line-of-sight propagation or non-line-of-sight propagation based at least in part on the phase variance.

A method for providing real-time propagation analysis is provided. The method includes receiving a request for propagation analysis from a wireless communication device located at a specified geographic location, selecting a propagation model from a plurality of propagation models based on available databases for the geographic location, generating a path loss estimation using the selected propagation model, and providing the path loss estimation to the wireless communication device.

Systems, methods and apparatus for spectrum data management for a radio frequency (RF) environment are disclosed. An apparatus comprises at least one receiver, an automatic signal detection (ASD) module, and a learning and conflict detection engine. The apparatus is at the edge of a communication network. The at least one receiver processes RF energy received from the RF environment, thereby generating processed data. The ASD module is configured to extract meta data and detect anomaly based on the processed data. The learning and conflict detection engine is configured for conflict recognition and anomaly identification based on the processed data. The apparatus is operable to generate at least one report for the RF environment.

A test system for testing follower jammer robustness of a radio is described. The test system comprises a signal analyzer and a signal generator that are connected with each other via a communication connection. The signal analyzer is configured to record a radio communication signal and to convert the recorded radio communication signal to a processing signal that is compatible for the communication connection. The signal generator is configured to add at least one delayed signal to the processing signal. Further, a method of testing follower jammer robustness of a radio is described.

In some embodiments, a method obtains raw data from one or more packets received over a wireless channel via an antenna. The raw data comprises raw amplitude values and raw phase values. Each raw amplitude value and raw phase value corresponds to a respective OFDM symbol and subcarrier of a respective packet. The method further comprises processing the raw data according to an interference mitigation process and using the resulting calibrated amplitude values and calibrated phase values to determine weighted phase values. Each weighted phase value corresponds to a respective subcarrier. The method determines a phase variance for the antenna based on comparing the plurality of weighted phase values across the plurality of subcarriers. The method determines whether the wireless channel experiences line-of-sight propagation or non-line-of-sight propagation based at least in part on the phase variance.