Systems, methods, and devices for electronic spectrum management

• US 20170243138A1
• Filed: 05/05/2017
• Published: 08/24/2017
• Est. Priority Date: 03/15/2013
• Status: Active Grant

• Current Assignee: DGS Global Systems, Inc., Digital Global Systems, Inc.
• Original Assignee: DGS Global Systems, Inc.
• Inventors: Dzierwa, Ronald C., Garcia, Gabriel R., Carbajal, Daniel
• Granted Patent: US 10,257,728 B2
• Time in Patent Office: Days
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/022 Knowledge engineering; Knowledge acquisition
• CPC Class Codes: H04B 17/23 Indication means, e.g. displays, alarms, audible means
• CPC Class Codes: H04B 17/27 for locating or positioning the transmitter
• CPC Class Codes: H04B 17/309 Measuring or estimating channel quality parameters
• CPC Class Codes: H04B 17/318 Received signal strength
• CPC Class Codes: H04B 17/3911 Fading models or fading generators
• CPC Class Codes: H04W 16/14 Spectrum sharing arrangements between different networks
• CPC Class Codes: H04W 24/08 Testing, supervising or monitoring using real traffic
• CPC Class Codes: H04W 64/00 Locating users or terminals or network equipment for network management purposes, e.g. mobility management
• CPC Class Codes: H04W 64/006 with additional information processing, e.g. for direction or speed determination

Abstract

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.

Claims

1. A method for automatic signal detection in a radio-frequency (RF) environment, comprising:

learning the RF environment to a settled percent based on statistical learning techniques, thereby creating learning data;

forming a knowledge map based on the learning data;

automatically extracting at least one temporal feature of the RF environment from the knowledge map;

scrubbing a real-time spectral sweep against the knowledge map; and

detecting at least one signal in the RF environment.

2. The method of claim 1, wherein the knowledge map comprises an array of normal distributions, wherein each normal distribution corresponds to how often the power level at each frequency has been at a particular level.

3. The method of claim 1, further comprising creating a profile of the RF environment based on the knowledge map, wherein the profile comprises the highest power level at which each frequency has been seen during the predetermined period.

4. The method of claim 1, further comprising automatically fine-tuning a threshold of power level on a segmented basis while extracting at least one temporal feature from the knowledge map.

5. The method of claim 1, wherein the frequency resolution of the knowledge map is based on a Fast Fourier Transform (FFT) size setting.

6. The method of claim 1, further comprising periodically reevaluating the RF environment and updating the knowledge map.

7. The method of claim 1, further comprising sending a notification and/or an alarm to an operator after detecting the at least one signal.

8. The method of claim 1, further comprising displaying the knowledge map and/or detecting results in real time on a remote device.

9. The method of claim 1, wherein the settled percent is about 99.95%.

10. The method of claim 1, further comprising creating a channel plan based on the learning data, user input, and/or external databases.

11. A system for automatic signal detection in a radio-frequency (RF) environment, comprising:

at least one apparatus for detecting signals in the RF environment;

wherein the at least one apparatus is operable to sweep and learn the RF environment to a settled percent based on statistical learning techniques, thereby creating learning data;

wherein the at least one apparatus is operable to form a knowledge map based on the learning data;

wherein the at least one apparatus is operable to automatically extract at least one temporal feature of the RF environment from the knowledge map;

wherein the at least one apparatus is operable to scrub a real-time spectral sweep against the knowledge map; and

wherein the at least one apparatus is operable to detect at least one signal in the RF environment.

12. The system of claim 11, wherein the knowledge map comprises an array of normal distributions, wherein each normal distribution corresponds to how often the power level at each frequency has been at a particular level.

13. The system of claim 11, wherein the at least one apparatus and/or a remote device is operable to create a profile of the RF environment based on the knowledge map, wherein the profile comprises the highest power level at which each frequency has been seen during the predetermined period.

14. The system of claim 11, wherein the at least one apparatus automatically fine-tunes a threshold of power level on a segmented basis while extracting the at least one temporal feature from the knowledge map.

15. The system of claim 11, wherein the frequency resolution of the knowledge map is based on a Fast Fourier Transform (FFT) size setting.

16. The system of claim 11, wherein the at least one apparatus periodically reevaluates the RF environment and updates the knowledge map.

17. The system of claim 11, wherein the at least one apparatus is operable to send a notification and/or an alarm to an operator after detecting the at least one signal.

18. The system of claim 11, further comprising a remote device in network-based communication with the at least one apparatus, wherein the knowledge map and detecting results are displayed on the remote device in real time.

19. The system of claim 11, wherein the settled percent is about 99.95%.

20. The system of claim 11, wherein the at least one apparatus and/or a remote device is operable to create a channel plan based on the learning data, user input, and/or external databases.