An apparatus for automatic signal detection in a radio-frequency environment, comprising: at least one receiver and at least one processor coupled with at least one memory; wherein the apparatus is operable to process data at an edge of a communication network; wherein the apparatus is operable to sweep and learn the radio-frequency environment during a predetermined time period and extract at least one time-frequency feature based on statistical learning techniques, thereby creating learning data including power level measurements of the radio-frequency environment; wherein the apparatus is operable to create a knowledge map comprising the at least one time-frequency feature based on the power level measurements; wherein the apparatus is operable to scrub a real-time spectral sweep against the knowledge map; wherein the apparatus is operable to calculate a first derivative of the power level measurements and a second derivative of the power level measurements; wherein the apparatus is operable to select most prominent derivatives of the first derivative and the second derivative; wherein the apparatus is operable to create impressions of the radio-frequency environment using the learning data and a machine learning algorithm; wherein the apparatus is operable to detect at least one signal in the radio-frequency environment based on matched positive and negative gradients; wherein the apparatus is operable to average the real-time spectral sweep, remove areas identified by the matched positive and negative gradients, and connect points between removed areas to determine a baseline; and wherein the apparatus is operable to subtract the baseline from the real-time spectral sweep to reveal the at least one signal. at least one receiver and at least one processor coupled with at least one memory; wherein the apparatus is operable to process data at an edge of a communication network; wherein the apparatus is operable to sweep and learn the radio-frequency environment during a predetermined time period and extract at least one time-frequency feature based on statistical learning techniques, thereby creating learning data including power level measurements of the radio-frequency environment; wherein the apparatus is operable to create a knowledge map comprising the at least one time-frequency feature based on the power level measurements; wherein the apparatus is operable to scrub a real-time spectral sweep against the knowledge map; wherein the apparatus is operable to calculate a first derivative of the power level measurements and a second derivative of the power level measurements; wherein the apparatus is operable to select most prominent derivatives of the first derivative and the second derivative; wherein the apparatus is operable to create impressions of the radio-frequency environment using the learning data and a machine learning algorithm; wherein the apparatus is operable to detect at least one signal in the radio-frequency environment based on matched positive and negative gradients; wherein the apparatus is operable to average the real-time spectral sweep, remove areas identified by the matched positive and negative gradients, and connect points between removed areas to determine a baseline; and wherein the apparatus is operable to subtract the baseline from the real-time spectral sweep to reveal the at least one signal. <EOS>