# US 11,445,378 B2 · System, method, and apparatus for providing dynamic, prioritized spectrum management and utilization

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- Number: US 11,445,378 B2
- Document ID: US11445378B2
- Status: Granted
- Kind: B2
- Application number: 17/461,267
- Filed: 2021-08-30
- Published: 2021-12-16
- Granted: 2022-09-13
- Earliest claimed: 2020-05-01
- Assignees: Digital Global Systems, Inc.
- Applicants: Digital Global Systems, Inc.
- Inventors: Armando Montalvo; Dwight Inman; Edward Hummel
- Web page: https://patentgrep.com/patent/US11445378B2

## CPC

- H04W 16/14: Spectrum sharing arrangements {between different networks}
- G06F 30/27: using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
- G06N 3/02: Neural networks
- G06N 3/0464: Convolutional networks [CNN, ConvNet]
- G06N 5/022: Knowledge engineering; Knowledge acquisition
- G06N 5/04: Inference or reasoning models
- G06N 20/00: Machine learning
- G06N 20/10: using kernel methods, e.g. support vector machines [SVM]
- G06N 20/20: Ensemble learning
- H04L 41/0893: Assignment of logical groups to network elements
- H04L 41/16: using machine learning or artificial intelligence
- H04W 16/10: Dynamic resource partitioning
- H04W 24/02: Arrangements for optimising operational condition
- H04W 72/0453: Resources in frequency domain, e.g. a carrier in FDMA
- G06N 3/042: Knowledge-based neural networks; Logical representations of neural networks
- G06N 3/045: Combinations of networks
- H04L 41/0894: Policy-based network configuration management
- H04W 24/08: Testing, {supervising or monitoring} using real traffic
- Y04S 40/00: Systems for electrical power generation, transmission, distribution or end-user application management characterised by the use of communication or information technologies, or communication or information technology specific aspects supporting them
- H04W: WIRELESS COMMUNICATION NETWORKS (broadcast communication H04H; communication systems using wireless links for non-selective communication, e.g. wireless extensions H04M1/72)
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M)
- Y04S: SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS

## Abstract

Systems, methods, and apparatuses for providing dynamic, prioritized spectrum utilization management. The system includes at least one monitoring sensor, at least one data analysis engine, at least one application, a semantic engine, a programmable rules and policy editor, a tip and cue server, and/or a control panel. The tip and cue server is operable utilize the environmental awareness from the data processed by the at least one data analysis engine in combination with additional information to create actionable data.

## Claims

### Claim 1

A system for spectrum management in an electromagnetic environment comprising:
at least one monitoring sensor operable to monitor the electromagnetic environment and create measured data based on the electromagnetic environment;
at least one data analysis engine for analyzing the measured data;
a semantic engine including a language dictionary and a programmable rules and policy editor, wherein the semantic engine is in network communication with the at least one data analysis engine; and
a tip and cue server;
wherein the at least one data analysis engine includes a detection engine and a learning engine, wherein the detection engine is operable to automatically detect at least one signal of interest, and wherein the learning engine is operable to learn the electromagnetic environment;
wherein the programmable rules and policy editor includes at least one rule and/or at least one policy, and wherein one or more of the at least one rule and/or the at least one policy is defined by at least one customer;
wherein the semantic engine is operable to create a semantic map including target data using semantic fingerprinting and a Boolean vector; and
wherein the tip and cue server is operable to use analyzed data from the at least one data analysis engine and information from the semantic engine to create actionable data.

### Claim 2 (depends on 1)

The system of claim 1, wherein the tip and cue server is operable to activate an alarm and/or provide at least one report based on the actionable data.

### Claim 3 (depends on 1)

The system of claim 1, wherein the at least one monitoring sensor includes at least one antenna, at least one antenna array, at least one radio server, and/or at least one software defined radio.

### Claim 4 (depends on 1)

The system of claim 1, wherein one or more of the at least one monitoring sensor is integrated with at least one camera to capture video and/or still images.

### Claim 5 (depends on 1)

The system of claim 1, wherein the at least one data analysis engine further includes an identification engine, a classification engine, and a geolocation engine.

### Claim 6 (depends on 1)

The system of claim 1, further including a resource brokerage application, wherein the resource brokerage application is operable to optimize resources to improve performance of at least one customer application and/or at least one customer device.

### Claim 7 (depends on 1)

The system of claim 1, further including a certification and compliance application, wherein the certification and compliance application is operable to determine if at least one customer application and/or at least one customer device is behaving according to the at least one rule and/or the at least one policy.

### Claim 8 (depends on 1)

The system of claim 1, further including a survey occupancy application, wherein the survey occupancy application is operable to determine occupancy in frequency bands and schedule occupancy in at least one frequency band.

### Claim 9 (depends on 1)

The system of claim 1, wherein the actionable data indicates that one or more of the at least one signal of interest is behaving like a drone.

### Claim 10 (depends on 1)

The system of claim 1, wherein the semantic engine is operable to receive queries, searches, and/or search-related functions using natural language.

### Claim 11 (depends on 1)

The system of claim 1, wherein the learning engine is operable to use machine learning (ML), artificial intelligence (AI), deep learning (DL), neural networks (NNs), artificial neural networks (ANNs), support vector machines (SVMs), Markov decision process (MDP), natural language processing (NLP), control theory, and/or statistical learning techniques.

### Claim 12 (depends on 1)

The system of claim 1, wherein the learning engine is operable to compute a set of possible conditional probabilities depicting a set of all possible outputs based on input measurements to provide a predicted outcome using a data model, and wherein the predicted outcome represents an outcome with the least probability of error and/or a false alarm.

### Claim 13 (depends on 1)

The system of claim 1, wherein the learning engine is operable to use third party data, wherein the third party data includes social media, population, real estate, traffic, geographic information system (GIS), network, signal site, site issue, and/or crowdsourced information.

### Claim 14 (depends on 1)

The system of claim 1, wherein the learning engine is operable to determine whether a data set processed and/or analyzed represents a sufficient statistical data set.

### Claim 15 (depends on 1)

The system of claim 1, wherein the learning engine includes a learning engine software development kit (SDK) operable to manage system resources relating to monitoring, logging, and/or organizing learning aspects of the system.

### Claim 16

A system for spectrum management in an electromagnetic environment comprising:
at least one monitoring sensor operable to monitor the electromagnetic environment and create measured data based on the electromagnetic environment;
at least one data analysis engine for analyzing the measured data;
a semantic engine including a language dictionary and a programmable rules and policy editor, wherein the semantic engine is in network communication with the at least one data analysis engine; and
a tip and cue server;
wherein the at least one data analysis engine includes a detection engine, an identification engine, a classification engine, a geolocation engine, and a learning engine, wherein the detection engine is operable to automatically detect at least one signal of interest, and wherein the learning engine is operable to learn the electromagnetic environment;
wherein the learning engine is operable to learn information from the detection engine, the classification engine, the identification engine, and/or the geolocation engine;
wherein the programmable rules and policy editor includes at least one rule and/or at least one policy, and wherein one or more of the at least one rule and/or the at least one policy is defined by at least one customer;
wherein the semantic engine is operable to create a semantic map including target data using semantic fingerprinting and a Boolean vector; and
wherein the tip and cue server is operable to use analyzed data from the at least one data analysis engine and information from the semantic engine to create actionable data.

### Claim 17 (depends on 16)

The system of claim 16, wherein the learning engine is operable to use machine learning (ML), artificial intelligence (AI), deep learning (DL), neural networks (NNs), artificial neural networks (ANNs), support vector machines (SVMs), Markov decision process (MDP), natural language processing (NLP), control theory, and/or statistical learning techniques.

### Claim 18 (depends on 16)

The system of claim 16, wherein the learning engine is operable to use third party data, wherein the third party data includes social media, population, real estate, traffic, geographic information system (GIS), network, signal site, site issue, and/or crowdsourced information.

### Claim 19 (depends on 16)

The system of claim 16, wherein the learning engine is operable to determine whether a data set processed and/or analyzed represents a sufficient statistical data set.

### Claim 20 (depends on 16)

The system of claim 16, wherein the learning engine includes a learning engine software development kit (SDK) operable to manage system resources relating to monitoring, logging, and/or organizing learning aspects of the system.

### Claim 21

A method for spectrum management in an electromagnetic environment comprising:
providing a semantic engine including a language dictionary and a programmable rules and policy editor, wherein the programmable rules and policy editor includes at least one rule and/or at least one policy, and wherein one or more of the at least one rule and/or the at least one policy is defined by at least one customer;
monitoring the electromagnetic environment using at least one monitoring sensor and creating measured data based on the electromagnetic environment;
analyzing the measured data using at least one data analysis engine, thereby creating analyzed data, wherein the at least one data analysis engine includes a detection engine and a learning engine, wherein the at least one data analysis engine is in network communication with the semantic engine;
learning the electromagnetic environment using the learning engine;
automatically detecting at least one signal of interest using the detection engine;
the semantic engine creating a semantic map including target data using semantic fingerprinting and a Boolean vector;
creating actionable data using a tip and cue server based on the analyzed data from the at least one data analysis engine and information from the semantic engine.

### Claim 22 (depends on 21)

The method of claim 21, further including the tip and cue server activating an alarm and/or providing at least one report based on the actionable data.

### Claim 23 (depends on 21)

The method of claim 21, wherein the learning engine is operable to use machine learning (ML), artificial intelligence (AI), deep learning (DL), neural networks (NNs), artificial neural networks (ANNs), support vector machines (SVMs), Markov decision process (MDP), natural language processing (NLP), control theory, and/or statistical learning techniques.

### Claim 24 (depends on 21)

The method of claim 21, wherein the learning engine is operable to use third party data, wherein the third party data includes social media, population, real estate, traffic, geographic information system (GIS), network, signal site, site issue, and/or crowdsourced information.

### Claim 25 (depends on 21)

The method of claim 21, further including the learning engine managing system resources relating to monitoring, logging, and/or organizing learning aspects of the system using a learning engine software development kit (SDK).

### Claim 26 (depends on 21)

The method of claim 21, further including the learning engine determining whether a data set processed and/or analyzed represents a sufficient statistical data set.

### Claim 27

A method for spectrum management in an electromagnetic environment comprising:
providing a semantic engine including a language dictionary and a programmable rules and policy editor, wherein the programmable rules and policy editor includes at least one rule and/or at least one policy, and wherein one or more of the at least one rule and/or the at least one policy is defined by at least one customer; monitoring the electromagnetic environment using at least one monitoring sensor and creating measured data based on the electromagnetic environment;
analyzing the measured data using at least one data analysis engine, thereby creating analyzed data, wherein the at least one data analysis engine includes a detection engine, a classification engine, an identification engine, a geolocation engine, and a learning engine, wherein the at least one data analysis engine is in network communication with the semantic engine;
learning the electromagnetic environment using the learning engine;
automatically detecting at least one signal of interest using the detection engine;
classifying the at least one signal of interest using the classification engine;
identifying the at least one signal of interest using the identification engine;
determining a location of the at least one signal of interest using the geolocation engine;
the semantic engine creating a semantic map including target data using semantic fingerprinting and a Boolean vector;
creating actionable data using a tip and cue server based on the analyzed data from the at least one data analysis engine and information from the semantic engine;
wherein the learning engine is operable to learn information from the detection engine, the classification engine, the identification engine, and/or the geolocation engine.

### Claim 28 (depends on 27)

The method of claim 27, wherein the learning engine is operable to use machine learning (ML), artificial intelligence (AI), deep learning (DL), neural networks (NNs), artificial neural networks (ANNs), support vector machines (SVMs), Markov decision process (MDP), natural language processing (NLP), control theory, and/or statistical learning techniques.

### Claim 29 (depends on 27)

The method of claim 27, wherein the learning engine is operable to use third party data, wherein the third party data includes social media, population, real estate, traffic, geographic information system (GIS), network, signal site, site issue, and/or crowdsourced information.

### Claim 30 (depends on 27)

The method of claim 27, further including the learning engine managing system resources relating to monitoring, logging, and/or organizing learning aspects of the system using a learning engine software development kit (SDK).