| CPC H04W 24/08 (2013.01) [H04B 17/23 (2015.01); H04B 17/27 (2015.01); H04B 17/309 (2015.01); H04B 17/318 (2015.01); H04W 4/029 (2018.02); H04W 16/14 (2013.01); H04W 24/10 (2013.01); H04W 52/0203 (2013.01); H04W 64/006 (2013.01); H04W 72/0453 (2013.01); H04W 72/0473 (2013.01); H04B 17/3911 (2015.01)] | 20 Claims |

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1. An apparatus for spectrum data management for a radio frequency (RF) environment, comprising:
at least one receiver and an automatic signal detection (ASD) module;
wherein the ASD module is operable for signal recognition based on temporal feature extraction and a machine learning (ML) algorithm;
wherein the ML algorithm is an artificial neural network (ANN) algorithm;
wherein a sensitivity of the ASD module is operable to be fine-tuned based on dynamic feedback;
wherein the dynamic feedback is processed real-time events from the ASD module;
wherein a threshold bar for detection of signals in the RF environment by the ASD module is not fixed;
wherein the apparatus is operable to form a knowledge map based on power level measurements of the RF environment and the ML algorithm;
wherein the apparatus is operable to scrub a real-time spectral sweep against the knowledge map;
wherein the apparatus is operable to determine a baseline;
wherein the apparatus is operable to smooth the real-time spectral sweep with a correction vector and at least one smoothing filter, wherein the correction vector is determined according to the real-time spectral sweep;
wherein the apparatus is operable to apply a gradient detection algorithm to smoothed signal data based on the ML algorithm;
wherein the apparatus is operable to use gradients from the gradient detection algorithm and the at least one smoothing filter to create a calibration vector;
wherein the apparatus is operable to use the calibration vector to de-bias raw signal data based on the ML algorithm;
wherein the apparatus is operable to subtract the baseline from the real-time spectral sweep to reveal at least one signal;
wherein the apparatus is operable to perform a second smoothing filter only on frequencies outside a frequency range of the at least one signal;
wherein the ASD module is operable to perform blind detection based on the ML algorithm;
wherein the ASD module and a temporal feature extraction (TFE) function are operable to identify a number of users on a network;
wherein the apparatus is operable to generate at least one report for the RF environment;
wherein the apparatus is operable to process signal data using compressed data for deltas, thereby generating processed data;
wherein the deltas are differentials from the baseline and minimize data sets or sample data required for comparisons and/or analytics; and
wherein the at least one report is a variance report, a power usage report, an RF survey report, a signal report and/or an optimization report.
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