IP Library Granted Patent US 12,520,162
Granted Patent B2
US 12,520,162 · App. 19/189,617 · Granted Jan 6, 2026

System, method, and apparatus for providing optimized network resources

Inventor: Armando Montalvo (Winter Garden, FL)
Assignee: Digital Global Systems, Inc.
H04W16/10H04W24/02H04W24/08H04W28/0925H04W28/0967H04W72/0453H04W16/14
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Quick Facts
Patent No.
US 12,520,162
App. No.
19/189,617
Granted
Jan 6, 2026
Kind
B2
Abstract

Systems, methods, and apparatuses for providing optimization of network resources. The system is operable to monitor the electromagnetic environment, analyze the electromagnetic environment, and extract environmental awareness of the electromagnetic environment. The system extracts the environmental awareness of the electromagnetic environment by including customer goals. The system is operable to use the environmental awareness with the customer goals and/or user defined policies and rules to extract actionable information to help the customer optimize the network resources.

Claims (37)

1 . A system for spectrum utilization management in an electromagnetic environment comprising:

at least one sensor unit configured to measure radiofrequency (RF) data; and

an RF analysis engine in the at least one sensor unit configured to extract physical layer data from measured RF data;

wherein the RF analysis engine is configured to analyze and/or process the extracted physical layer data to create analyzed and/or processed physical layer data;

wherein the at least one sensor unit and the RF analysis engine are configured to send the analyzed and/or processed physical layer data to a distributed data unit (DDU), a centralized unit (CU), at least one radio unit (RU), and/or a Multi-Access Edge Computing (MEC) layer;

wherein the RF analysis engine includes a Fast Fourier Transform (FFT) engine configured to provide for a duty cycle using multiple four streams and one second per stream to reduce calculations and provide for increased real-time sampling;

wherein the RF analysis engine is configured to use the FFT engine, condition in-phase and quadrature (I&Q) data, perform channel extraction and/or classification, and aggregate the physical layer data from the at least one RU to support Layer 2 processing at the DDU to create the analyzed and/or processed physical layer data;

wherein the analyzed and/or processed extracted physical layer data is used by the DDU, the CU, the at least one RU, and/or the MEC layer to create actionable data for optimizing network resources; and

wherein the RF analysis engine is configured to send the actionable data to a Layer 3 (L3) network layer and the MEC layer for further optimizing the network resources based on customer-defined inputs and predetermined policies.

2 . The system of claim 1 , wherein the actionable data includes data for modulation and/or demodulation, beam forming, and/or interference detection.

3 . The system of claim 1 , wherein the at least one sensor unit further includes a programmable rules and policy editor, wherein the programmable rules and policy editor is configured to communicate the analyzed or processed physical layer data to the MEC layer.

4 . The system of claim 1 , wherein the RF analysis engine includes a channelizer.

5 . The system of claim 1 , wherein the RF analysis engine includes a blind classification engine.

6 . The system of claim 1 , wherein the RF analysis engine is configured to perform a priori detection.

7 . The system of claim 1 , wherein the RF analysis engine is configured to provide Noise Floor Extension (NFE) for noise compensation.

8 . The system of claim 1 , wherein the analyzed and/or processed physical layer data is provided to the DDU, the CU, the at least one RU, and/or the MEC layer in real-time.

9 . The system of claim 1 , wherein the analyzed or processed extracted physical layer data is used by the DDU, the CU, the at least one RU, and/or the MEC layer to create actionable data for spectrum sharing in the electromagnetic environment in real time.

10 . The system of claim 1 , wherein the RF analysis engine is configured to provide a recommendation for a change in at least one physical layer parameter based on the analyzed and/or processed physical layer data.

11 . A system for spectrum utilization management in an electromagnetic environment comprising:

at least one sensor unit configured to measure radiofrequency (RF) data; and

an RF analysis engine in the at least one sensor unit configured to extract physical layer data from the measured RF data;

wherein the RF analysis engine is configured to analyze and/or process the extracted physical layer data to create analyzed and/or processed physical layer data;

wherein the at least one sensor unit and/or the RF analysis engine is configured to send the analyzed and/or processed physical layer data to a remote device;

wherein the RF analysis engine includes a Fast Fourier Transform (FFT) engine configured to provide for a duty cycle using multiple four streams and one second per stream to reduce calculations and provide for increased real-time sampling;

wherein the RF analysis engine is configured to use the FFT engine, condition in-phase and quadrature (I&Q) data, perform channel extraction and/or classification, and aggregate the physical layer data from the at least one RU to support Layer 2 processing at the DDU to create the analyzed and/or processed physical layer data;

wherein the analyzed or processed extracted physical layer data is used by the remote device to create actionable data for optimizing network resources; and

wherein the RF analysis engine is configured to send the actionable data to a Layer 3 (L3) network layer and a MEC layer for further optimizing the network resources based on customer-defined inputs and predetermined policies.

12 . The system of claim 11 , wherein the actionable data includes real-time data and/or dynamic spectrum sharing data.

13 . An apparatus for dynamic spectrum utilization management in an electromagnetic environment comprising:

the at least one sensor unit configured to measure radiofrequency (RF) data and includes an RF analysis engine operable to extract physical layer data from the measured RF data;

wherein the RF analysis engine is configured to analyze and/or process the extracted physical layer data to create analyzed and/or processed physical layer data; and

wherein the RF analysis engine includes a Fast Fourier Transform (FFT) engine configured to provide for a duty cycle using multiple four streams and one second per stream to reduce calculations and provide for increased real-time sampling;

wherein the RF analysis engine is configured to use the FFT engine, condition in-phase and quadrature (I&Q) data, perform channel extraction and/or classification, and aggregate the physical layer data from the at least one RU to support Layer 2 processing at the DDU to create the analyzed and/or processed physical layer data;

wherein the RF analysis engine is operable to provide a recommendation for a change in at least one physical layer parameter for optimizing network resources based on the analyzed and/or processed extracted physical layer data; and

wherein the RF analysis engine is configured to send the actionable data to a Layer 3 (L3) network layer and a MEC layer for further optimizing the network resources based on customer-defined inputs and predetermined policies.

14 . The apparatus of claim 13 , wherein the RF analysis engine includes a channelizer.

15 . The apparatus of claim 13 , wherein the RF analysis engine is configured to perform a priori detection.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Apr 25, 2025
From: MONTALVO, ARMANDO
To: DIGITAL GLOBAL SYSTEMS, INC.
Reel/Frame 070948/0413 →
Continuity (12)
Continuation 18941770 · Nov 8, 2024
Continuation 18756930 · Jun 27, 2024
Continuation 18630528 · Apr 9, 2024
Continuation 18526329 · Dec 1, 2023
Continuation 18237970 · Aug 25, 2023
Continuation In Part 18086115 · Dec 21, 2022
Continuation In Part 18085904 · Dec 21, 2022
Continuation 18085791 · Dec 21, 2022
Continuation 18085733 · Dec 21, 2022
Continuation In Part 17901035 · Sep 1, 2022
Provisional Application 63370184 · Aug 2, 2022
Related Publication 20250274768A1 · Aug 28, 2025
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T. O'Shea and J. Hoydis, “An Introduction to Deep Learning for the Physical Layer,” in IEEE Transactions on Cognitive Communications and Networking, vol. 3, No. 4, pp. 563-575, Dec. 2017, doi: 10.1109/TCCN.2017.2758370. [cited by applicant]