IP Library Granted Patent US 12,273,730
Granted Patent B2
US 12,273,730 · App. 18/825,687 · Granted Apr 8, 2025

System, method, and apparatus for providing optimized network resources

Inventor: Armando Montalvo (Winter Garden, FL)
Assignee: Digital Global Systems, Inc.
H04W16/10H04W24/02H04W24/08H04W24/10H04W28/24H04W16/14H04W24/04H04W28/0268
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Quick Facts
Patent No.
US 12,273,730
App. No.
18/825,687
Granted
Apr 8, 2025
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 (38)

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

a Multi-Access Edge Computing (MEC) layer in a network slice or a subnetwork;

a wireless network resource optimization application configured to receive measured data from the electromagnetic environment; and

at least one data analysis engine for analyzing the measured data to create analyzed data;

wherein the analyzed data is represented in a vector ensemble class for each signal in the electromagnetic environment;

wherein the wireless network resource optimization application is operable to use the analyzed data and a customer goals index vector to create actionable data for optimizing network resources; and

wherein the customer goals index vector is a vector of binary values.

2. The system of claim 1 , wherein the actionable data for optimizing network resources includes data for reconfiguring at least one parameter of a network.

3. The system of claim 1 , wherein the actionable data for optimizing network resources includes data for changing at least one physical layer parameter of one or more customer devices and/or applications.

4. The system of claim 1 , wherein at least one monitoring sensor creates the measured data, and wherein the at least one monitoring sensor is included in a base station and/or at least one spectrum monitoring unit.

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

6. The system of claim 1 , wherein the MEC layer includes a MEC orchestrator operable to interact as an application function with a network exposure function (NEF).

7. The system of claim 6 , wherein the NEF is configured to access a policy control function (PCF) operable to manage rules and policies.

8. The system of claim 6 , wherein the NEF provides network information to the MEC layer.

9. The system of claim 1 , wherein the wireless network resource optimization application generates at least one radio access network (RAN) command to change at least one RAN parameter.

10. The system of claim 1 , further comprising at least one RAN interface operable to provide resource allocation recommendations.

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

at least one monitoring sensor operable to monitor the electromagnetic environment and to create measured data;

a Multi-Access Edge Computing (MEC) layer;

a wireless network resource optimization application configured to receive the measured data; and

at least one data analysis engine for analyzing the measured data to create analyzed data;

wherein the analyzed data is represented in a vector ensemble class for each signal in the electromagnetic environment;

wherein the wireless network resource optimization application is operable to use the analyzed data and a customer goals index vector to create actionable data for optimizing network resources; and

wherein the customer goals index vector is a vector of binary values.

12. The system of claim 11 , wherein the MEC layer includes a MEC orchestrator operable to interact as an application function with a network exposure function (NEF).

13. The system of claim 12 , wherein the NEF provides network information to the MEC layer.

14. The system of claim 11 , wherein the wireless network resource optimization application is operable to activate an alarm.

15. The system of claim 11 , wherein the at least one data analysis engine further includes a detection engine, a learning engine, an identification engine, a classification engine, and/or a geolocation engine.

16. The system of claim 11 , wherein the wireless network resource optimization application creates the actionable data in real time or in near-real time.

17. The system of claim 11 , further comprising a machine learning (ML) engine operable to learn the electromagnetic environment and make predictions about the electromagnetic environment.

18. The system of claim 11 , wherein the at least one monitoring sensor is included in a base station and/or at least one spectrum monitoring unit.

19. A method for dynamic spectrum utilization management in an electromagnetic environment comprising:

obtaining electromagnetic environmental awareness information in a vector ensemble class for each signal in the electromagnetic environment;

analyzing each signal for possible interactions based on center frequency or bandwidth;

creating a set by adding the possible interactions to the vector ensemble class; and

combining the set with a customer goals index vector to create actionable data for optimization of network resources of the wireless network;

wherein the customer goals index vector is a vector of binary values.

20. The method of claim 19 , further comprising optimizing the network resources by changing at least one physical layer parameter of one or more customer devices and/or applications or by reconfiguring at least one parameter of a network.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Sep 11, 2024
From: MONTALVO, ARMANDO
To: DIGITAL GLOBAL SYSTEMS, INC.
Reel/Frame 068555/0619 →
Continuity (6)
Continuation 18633934 · Apr 12, 2024
Continuation 18336462 · Jun 16, 2023
Continuation 18101899 · Jan 26, 2023
Continuation 17901035 · Sep 1, 2022
Provisional Application 63370184 · Aug 2, 2022
Related Publication 20240430691A1 · Dec 26, 2024
References Cited (236)
US 6990087B2 · Rao et al. · 2006 [cited by applicant]
US 7215716B1 · Smith · 2007 [cited by applicant]
US 7289733B1 · He · 2007 [cited by applicant]
US 7408907B2 · Diener · 2008 [cited by applicant]
US 7471654B2 · Mueckenheim et al. · 2008 [cited by applicant]
US 8175539B2 · Diener et al. · 2012 [cited by applicant]
US 8229368B1 · Immendorf et al. · 2012 [cited by applicant]
US 8254393B2 · Horvitz · 2012 [cited by applicant]
US 8301075B2 · Sherman et al. · 2012 [cited by applicant]
US 8515473B2 · Mody et al. · 2013 [cited by applicant]
US 8675781B2 · Adnani et al. · 2014 [cited by applicant]
US 8744466B2 · Hirano et al. · 2014 [cited by applicant]
US 8886794B2 · Adnani et al. · 2014 [cited by applicant]
US 8972311B2 · Srikanteswara et al. · 2015 [cited by applicant]
US 9197260B2 · Adnani et al. · 2015 [cited by applicant]
US 9246576B2 · Yanai et al. · 2016 [cited by applicant]
US 9338685B2 · Saghir et al. · 2016 [cited by applicant]
US 9350404B2 · Adnani et al. · 2016 [cited by applicant]
US 9356727B2 · Immendorf et al. · 2016 [cited by applicant]
US 9397619B2 · Lozhkin · 2016 [cited by applicant]
US 9408210B2 · Pikhletsky et al. · 2016 [cited by applicant]
US 9439078B2 · Menon et al. · 2016 [cited by applicant]
US 9538040B2 · Goergen et al. · 2017 [cited by applicant]
US 9538528B2 · Wagner et al. · 2017 [cited by applicant]
US 9572055B2 · Immendorf et al. · 2017 [cited by applicant]
US 9578516B2 · Liu et al. · 2017 [cited by applicant]
US 9635669B2 · Gormley et al. · 2017 [cited by applicant]
US 9674836B2 · Gormley et al. · 2017 [cited by applicant]
US 9686789B2 · Gormley et al. · 2017 [cited by applicant]
US 9749902B2 · Horn et al. · 2017 [cited by applicant]
US 9769834B2 · Immendorf et al. · 2017 [cited by applicant]
US 9819441B2 · Immendorf et al. · 2017 [cited by applicant]
US 9900899B2 · Jiang et al. · 2018 [cited by applicant]
US 9923700B2 · Gormley et al. · 2018 [cited by applicant]
US 9942775B2 · Yun et al. · 2018 [cited by applicant]
US 9989633B1 · Pandey et al. · 2018 [cited by applicant]
US 10051518B2 · Horn et al. · 2018 [cited by applicant]
US 10070444B2 · Markwart et al. · 2018 [cited by applicant]
US 10104559B2 · Immendorf et al. · 2018 [cited by applicant]
US 10194324B2 · Yun et al. · 2019 [cited by applicant]
US 10349309B2 · Horn et al. · 2019 [cited by applicant]
US 10356661B2 · Horn et al. · 2019 [cited by applicant]
US 10389616B2 · Ryan et al. · 2019 [cited by applicant]
US 10393784B2 · Logan et al. · 2019 [cited by applicant]
US 10402689B1 · Bogdanovych et al. · 2019 [cited by applicant]
US 10405159B2 · Dauneria et al. · 2019 [cited by applicant]
US 10432798B1 · Wong et al. · 2019 [cited by applicant]
US 10462675B2 · Gosh et al. · 2019 [cited by applicant]
US 10477342B2 · Williams · 2019 [cited by applicant]
US 10506543B1 · Edge et al. · 2019 [cited by applicant]
US 10536210B2 · Zhao et al. · 2020 [cited by applicant]
US 10541712B1 · Ayala et al. · 2020 [cited by applicant]
US 10552738B2 · Holt et al. · 2020 [cited by applicant]
US 10582401B2 · Mengwasser et al. · 2020 [cited by applicant]
US 10592683B1 · Lim et al. · 2020 [cited by applicant]
US 10605890B1 · Yun et al. · 2020 [cited by applicant]
US 10700721B2 · Ayala et al. · 2020 [cited by applicant]
US 10701217B2 · Wong et al. · 2020 [cited by applicant]
US 10701574B2 · Gormley et al. · 2020 [cited by applicant]
US 10784974B2 · Menon · 2020 [cited by applicant]
US 10812992B1 · Tran et al. · 2020 [cited by applicant]
US 10813102B2 · Yun et al. · 2020 [cited by applicant]
US 10849180B2 · Karimli et al. · 2020 [cited by applicant]
US 10917797B2 · Menon et al. · 2021 [cited by applicant]
US 10952178B2 · Edge et al. · 2021 [cited by applicant]
US 10959203B2 · Edge et al. · 2021 [cited by applicant]
US 11012340B2 · Ryan et al. · 2021 [cited by applicant]
US 11018784B2 · Ryan et al. · 2021 [cited by applicant]
US 11018957B1 · Ezra et al. · 2021 [cited by applicant]
US 11019514B2 · Ayala et al. · 2021 [cited by applicant]
US 11032014B2 · O'Shea et al. · 2021 [cited by applicant]
US 11035972B2 · Colombo et al. · 2021 [cited by applicant]
US 11063653B2 · Ottersten et al. · 2021 [cited by applicant]
US 11096036B2 · Poornachandran et al. · 2021 [cited by applicant]
US 11101903B2 · Yun · 2021 [cited by applicant]
US 11115336B2 · Sabella et al. · 2021 [cited by applicant]
US 11153762B1 · Routt · 2021 [cited by applicant]
US 11190946B1 · Montalvo · 2021 [cited by applicant]
US 11202206B2 · Taneja et al. · 2021 [cited by applicant]
US 11206549B1 · Eyuboglu · 2021 [cited by applicant]
US 11259189B2 · Montalvo et al. · 2022 [cited by applicant]
US 11272372B2 · Montalvo et al. · 2022 [cited by applicant]
US 11277161B2 · Ayala et al. · 2022 [cited by applicant]
US 11277750B2 · Montalvo et al. · 2022 [cited by applicant]
US 11277751B2 · Montalvo · 2022 [cited by applicant]
US 11284267B2 · Montalvo et al. · 2022 [cited by applicant]
US 11310676B2 · Gormley et al. · 2022 [cited by applicant]
US 11334807B1 · O'Shea et al. · 2022 [cited by applicant]
US 11349582B2 · Yun et al. · 2022 [cited by applicant]
US 11394475B1 · Vaca et al. · 2022 [cited by applicant]
US 11395149B2 · Montalvo · 2022 [cited by applicant]
US 11412033B2 · Ganguli et al. · 2022 [cited by applicant]
US 11477787B2 · Ananth · 2022 [cited by applicant]
US 11540295B2 · Yun et al. · 2022 [cited by applicant]
US 11570627B1 · Montalvo · 2023 [cited by applicant]
US 11616279B2 · Brunette et al. · 2023 [cited by applicant]
US 11632762B2 · Chakraborty et al. · 2023 [cited by applicant]
US 11638160B2 · Montalvo et al. · 2023 [cited by applicant]
US 11653213B2 · Montalvo · 2023 [cited by applicant]
US 11659400B1 · Montalvo · 2023 [cited by applicant]
US 11659401B1 · Montalvo · 2023 [cited by applicant]
US 11665547B2 · Montalvo · 2023 [cited by applicant]
US 11683695B1 · Montalvo · 2023 [cited by applicant]
US 11700533B2 · Montalvo · 2023 [cited by applicant]
US 11711726B1 · Montalvo · 2023 [cited by applicant]
US 11711759B1 · Gupta et al. · 2023 [cited by applicant]
US 11751064B1 · Montalvo · 2023 [cited by applicant]
US 11843953B1 · Montalvo · 2023 [cited by applicant]
US 11849305B1 · Montalvo · 2023 [cited by applicant]
US 11930370B2 · Montalvo · 2024 [cited by applicant]
US 11968539B2 · Montalvo · 2024 [cited by applicant]
US 11997502B2 · Montalvo · 2024 [cited by applicant]
US 12022297B2 · Montalvo · 2024 [cited by applicant]
US 20040028003A1 · Diener et al. · 2004 [cited by applicant]
US 20100325621A1 · Andrade et al. · 2010 [cited by applicant]
US 20110083154A1 · Boersma · 2011 [cited by applicant]
US 20110090939A1 · Diener et al. · 2011 [cited by applicant]
US 20120120887A1 · Deaton et al. · 2012 [cited by applicant]
US 20130275346A1 · Srikanteswara et al. · 2013 [cited by applicant]
US 20130315112A1 · Gormley et al. · 2013 [cited by applicant]
US 20130331114A1 · Gormley et al. · 2013 [cited by applicant]
US 20140185580A1 · Fang et al. · 2014 [cited by applicant]
US 20140204766A1 · Immendorf et al. · 2014 [cited by applicant]
US 20140206279A1 · Immendorf et al. · 2014 [cited by applicant]
US 20140206343A1 · Immendorf et al. · 2014 [cited by applicant]
US 20140301216A1 · Immendorf et al. · 2014 [cited by applicant]
US 20140302796A1 · Gormley et al. · 2014 [cited by applicant]
US 20140335879A1 · Immendorf et al. · 2014 [cited by applicant]
US 20150016429A1 · Menon et al. · 2015 [cited by applicant]
US 20150215794A1 · Gormley et al. · 2015 [cited by applicant]
US 20150215949A1 · Gormley et al. · 2015 [cited by applicant]
US 20150245374A1 · Mitola et al. · 2015 [cited by applicant]
US 20150289265A1 · Gormley et al. · 2015 [cited by applicant]
US 20150296386A1 · Menon et al. · 2015 [cited by applicant]
US 20150350914A1 · Baxley et al. · 2015 [cited by applicant]
US 20160050690A1 · Yun et al. · 2016 [cited by applicant]
US 20160366685A1 · Gormley et al. · 2016 [cited by applicant]
US 20170041802A1 · Sun et al. · 2017 [cited by applicant]
US 20170064564A1 · Yun et al. · 2017 [cited by applicant]
US 20170148467A1 · Franklin et al. · 2017 [cited by applicant]
US 20170187450A1 · Jalali · 2017 [cited by applicant]
US 20170238201A1 · Gormley et al. · 2017 [cited by applicant]
US 20170245280A1 · Yi et al. · 2017 [cited by applicant]
US 20170280411A1 · Noonan · 2017 [cited by applicant]
US 20180041905A1 · Ashrafi · 2018 [cited by applicant]
US 20180070362A1 · Ryan et al. · 2018 [cited by applicant]
US 20180295607A1 · Lindoff et al. · 2018 [cited by applicant]
US 20180316627A1 · Cui et al. · 2018 [cited by applicant]
US 20180324595A1 · Shima · 2018 [cited by applicant]
US 20180343567A1 · Ashrafi · 2018 [cited by applicant]
US 20180351824A1 · Giust et al. · 2018 [cited by applicant]
US 20180352441A1 · Zheng et al. · 2018 [cited by applicant]
US 20180376006A1 · Russell et al. · 2018 [cited by applicant]
US 20190129407A1 · Cella et al. · 2019 [cited by applicant]
US 20190199756A1 · Correnti et al. · 2019 [cited by applicant]
US 20190339688A1 · Cella et al. · 2019 [cited by applicant]
US 20190342202A1 · Ryan et al. · 2019 [cited by applicant]
US 20190373428A1 · Baer · 2019 [cited by applicant]
US 20200007249A1 · Derr et al. · 2020 [cited by applicant]
US 20200036459A1 · Menon · 2020 [cited by applicant]
US 20200059800A1 · Menon et al. · 2020 [cited by applicant]
US 20200081484A1 · Lee et al. · 2020 [cited by applicant]
US 20200145032A1 · Ayala et al. · 2020 [cited by applicant]
US 20200145852A1 · Ayala et al. · 2020 [cited by applicant]
US 20200153467A1 · Ayala et al. · 2020 [cited by applicant]
US 20200153535A1 · Kankanamge et al. · 2020 [cited by applicant]
US 20200186265A1 · Yun · 2020 [cited by applicant]
US 20200187213A1 · Yun et al. · 2020 [cited by applicant]
US 20200213006A1 · Graham et al. · 2020 [cited by applicant]
US 20200217882A1 · Lee et al. · 2020 [cited by applicant]
US 20200336228A1 · Ryan et al. · 2020 [cited by applicant]
US 20200344619A1 · Gormley et al. · 2020 [cited by applicant]
US 20200383127A1 · Zhu et al. · 2020 [cited by applicant]
US 20200412749A1 · Rollet · 2020 [cited by applicant]
US 20210045127A1 · Yun et al. · 2021 [cited by applicant]
US 20210092647A1 · Yang et al. · 2021 [cited by applicant]
US 20210111953A1 · Hall et al. · 2021 [cited by applicant]
US 20210112436A1 · Hoffner et al. · 2021 [cited by applicant]
US 20210144517A1 · Guim Bernat et al. · 2021 [cited by applicant]
US 20210176613A1 · Purkayastha et al. · 2021 [cited by applicant]
US 20210182283A1 · Carney et al. · 2021 [cited by applicant]
US 20210194912A1 · Ward et al. · 2021 [cited by applicant]
US 20210203576A1 · Padfield et al. · 2021 [cited by applicant]
US 20210227427A1 · Mishra et al. · 2021 [cited by applicant]
US 20210266716A1 · Dowlatkhah et al. · 2021 [cited by applicant]
US 20210274412A1 · Dowlatkhah et al. · 2021 [cited by applicant]
US 20210288731A1 · Yun et al. · 2021 [cited by applicant]
US 20210289376A1 · Chou et al. · 2021 [cited by applicant]
US 20210390329A1 · Ren et al. · 2021 [cited by applicant]
US 20210392503A1 · Montalvo et al. · 2021 [cited by applicant]
US 20210409959A1 · Montalvo et al. · 2021 [cited by applicant]
US 20220158676A1 · Adnani et al. · 2022 [cited by applicant]
US 20220167182A1 · Ramamurthi et al. · 2022 [cited by applicant]
US 20220201525A1 · Adnani · 2022 [cited by applicant]
US 20220201556A1 · Yang et al. · 2022 [cited by applicant]
US 20220210688A1 · Baglin et al. · 2022 [cited by applicant]
US 20220254369A1 · Ryu et al. · 2022 [cited by applicant]
US 20220346029A1 · Al-Mufti et al. · 2022 [cited by applicant]
US 20220353732A1 · Filippou et al. · 2022 [cited by applicant]
US 20220377614A1 · Balakrishnan et al. · 2022 [cited by applicant]
US 20220386179A1 · Dhammawat et al. · 2022 [cited by applicant]
US 20230086899A1 · Banjade et al. · 2023 [cited by applicant]
US 20230090727A1 · Yun et al. · 2023 [cited by applicant]
US 20230110731A1 · Montalvo et al. · 2023 [cited by applicant]
US 20230116761A1 · Barry et al. · 2023 [cited by applicant]
US 20230179974A1 · Gadalin et al. · 2023 [cited by applicant]
US 20230180017A1 · Gadalin et al. · 2023 [cited by applicant]
US 20230199523A1 · Adnani · 2023 [cited by applicant]
US 20230209578A1 · Chakraborty et al. · 2023 [cited by applicant]
US 20230308885A1 · Sirotkin et al. · 2023 [cited by applicant]
US 20230354375A1 · Niu et al. · 2023 [cited by applicant]
US 20230354429A1 · Niu et al. · 2023 [cited by applicant]
US 20240040386A1 · Yao et al. · 2024 [cited by applicant]
US 20240048994A1 · Montalvo · 2024 [cited by applicant]
US 20240107323A1 · Montalvo · 2024 [cited by applicant]
US 20240107324A1 · Montalvo · 2024 [cited by applicant]
US 20240147246A1 · Montalvo · 2024 [cited by applicant]
US 20240155355A1 · Montalvo · 2024 [cited by applicant]
US 20240155358A1 · Montalvo · 2024 [cited by applicant]
US 20240155359A1 · Montalvo · 2024 [cited by applicant]
US 20240163679A1 · Montalvo · 2024 [cited by applicant]
US 20240163680A1 · Montalvo · 2024 [cited by applicant]
US 20240171984A1 · Montalvo · 2024 [cited by applicant]
US 20240171986A1 · Montalvo · 2024 [cited by applicant]
US 20240171987A1 · Montalvo · 2024 [cited by applicant]
US 20240196223A1 · Montalvo · 2024 [cited by applicant]
US 20240214824A1 · Montalvo · 2024 [cited by applicant]
US 20240244443A1 · Montalvo · 2024 [cited by applicant]
US 20240244453A1 · Montalvo · 2024 [cited by applicant]
US 20240259821A1 · Montalvo · 2024 [cited by applicant]
CN 114173379A · 2022 [cited by applicant]
WO 2018184682A1 · 2018 [cited by applicant]
WO 2023091664A1 · 2023 [cited by applicant]
S. Dörner, S. Cammerer, J. Hoydis and S. t. Brink, “Deep Learning Based Communication Over the Air,” in IEEE Journal of Selected Topics in Signal Processing, vol. 12, No. 1, pp. 132-143, Feb. 2018, doi: 10.1109/JSTSP.20… [cited by applicant]
T. J. O'Shea, K. Karra and T. C. Clancy, “Learning to communicate: Channel auto-encoders, domain specific regularizers, and attention,” 2016 IEEE International Symposium on Signal Processing and Information Technology (… [cited by applicant]
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]