IP Library Granted Patent US 12,445,858
Granted Patent B1
US 12,445,858 · App. 19/247,632 · Granted Oct 14, 2025

System, method, and apparatus for providing optimized network resources

Inventor: Armando Montalvo (Winter Garden, FL)
Assignee: Digital Global Systems, Inc.
H04W16/10H04W16/14H04W24/02H04W24/04H04W24/08H04W28/0925H04W28/0967H04W72/0453
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,445,858
App. No.
19/247,632
Granted
Oct 14, 2025
Kind
B1
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 (36)

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

a network slice or a subnetwork, wherein the network slice or the subnetwork includes a radio access network (RAN) and a Multi-Access Edge Computing (MEC) layer; and

a wireless network resource optimization application in the MEC layer including at least one data analysis engine and an inference engine;

wherein the data analysis engine analyzes measured data from the electromagnetic environment to create analyzed data;

wherein the inference engine utilizes statistical learning techniques and/or control theory to learn and/or make predictions about the electromagnetic environment based on the analyzed data;

wherein the inference engine identifies relevant information required by a customer application to optimize network resource allocation and/or to decrease an amount of the analyzed data that is required; and

wherein the wireless network resource optimization application is operable to use the analyzed data and the relevant information identified by the inference engine to create actionable data for optimizing network resources.

2. The system of claim 1 , wherein the MEC layer is operable to determine a location of at least one user equipment (UE) device.

3. The system of claim 2 , wherein the MEC layer includes procedures for migration and/or service continuity using MEC host pre-allocation based on a predicted future location of the at least one UE device.

4. The system of claim 1 , wherein the relevant information is used along with network information to identify physical layer resources required for the customer application.

5. The system of claim 1 , wherein the wireless network resource optimization application generates at least one RAN command to change at least one RAN parameter.

6. The system of claim 1 , wherein the network resources are optimized by reconfiguring at least one parameter of a core network and/or the MEC layer associated with the network slice or the subnetwork.

7. The system of claim 1 , wherein the wireless network resource optimization application analyzes detected signal information from the analyzed data or the measured data.

8. The system of claim 7 , wherein the detected signal information includes a center frequency and bandwidth of at least one signal.

9. The system of claim 8 , wherein the wireless network resource optimization application is operable to obtain statistical information and analyze possible interactions based on the center frequency and the bandwidth of each signal to create the analyzed data.

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

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

processing measured data from the electromagnetic environment using a data analysis engine within a wireless network resource optimization application in the MEC layer to generate analyzed data;

applying an inference engine within the wireless network resource optimization application to the analyzed data and identifying relevant information required by a customer application to optimize network resource allocation and/or reduce an amount of the analyzed data that is processed; and

generating actionable data for optimizing network resources based on the analyzed data and the relevant information identified by the inference engine;

wherein the inference engine utilizes statistical learning techniques and/or control theory to learn the electromagnetic environment.

11. The method of claim 10 , further comprising determining channel availability in the electromagnetic environment.

12. The method of claim 10 , further comprising providing a quality of a channel to a network operator and receiving a request for use of the channel from the network operator based on the quality of the channel.

13. The method of claim 10 , further comprising the MEC layer determining a location of at least one user equipment (UE) device.

14. The method of claim 10 , further comprising providing optimization of service to at least one user equipment (UE) device based on a predicted future location of at least one UE device.

15. The method of claim 10 , further comprising identifying physical layer resources required for the customer application using the relevant information and network information.

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

providing a Multi-Access Edge Computing (MEC) layer and radio access network (RAN) in a network slice or a subnetwork;

processing measured data from the electromagnetic environment using a data analysis engine within a wireless network resource optimization application in the MEC layer to generate analyzed data; and

applying an inference engine within the wireless network resource optimization application to the analyzed data and identifying relevant information required by a customer application to optimize network resource allocation and reduce an amount of the analyzed data that is required;

wherein the inference engine utilizes statistical learning techniques and/or control theory to learn the electromagnetic environment; and

wherein the inference engine is programmed according to customer goals regarding the customer application.

17. The method of claim 16 , further comprising determining channel availability in the electromagnetic environment.

18. The method of claim 16 , further comprising providing a quality of a channel to a network operator and receiving a request for use of the channel from the network operator based on the quality of the channel.

19. The method of claim 16 , further comprising the MEC layer providing procedures for migration and/or service continuity based on a predicted future location of at least one user equipment (UE) device.

20. The method of claim 16 , further comprising creating a relocation group including at least one MEC host pre-configured to run the customer application.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jul 11, 2025
From: MONTALVO, ARMANDO
To: DIGITAL GLOBAL SYSTEMS, INC.
Reel/Frame 071681/0210 →
Continuity (13)
Continuation 19047135 · Feb 6, 2025
Continuation 19016322 · Jan 10, 2025
Continuation 18825734 · Sep 5, 2024
Continuation 18764829 · Jul 5, 2024
Continuation 18415209 · Jan 17, 2024
Continuation 18405622 · Jan 5, 2024
Continuation 18240132 · Aug 30, 2023
Continuation 18086115 · Dec 21, 2022
Continuation In Part 18085904 · Dec 21, 2022
Continuation 18085733 · Dec 21, 2022
Continuation 18085791 · Dec 21, 2022
Continuation In Part 17901035 · Sep 1, 2022
Provisional Application 63370184 · Aug 2, 2022
References Cited (280)
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 20140036984A1 · Charbonneau et al. · 2014 [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 20180083812A1 · Williams · 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 · 2022 [cited by examiner]
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]
US 20240298184A1 · Montalvo · 2024 [cited by applicant]
US 20240340649A1 · Montalvo · 2024 [cited by applicant]
US 20240349065A1 · Montalvo · 2024 [cited by applicant]
US 20240349066A1 · Montalvo · 2024 [cited by applicant]
US 20240357364A1 · Montalvo · 2024 [cited by applicant]
US 20240357365A1 · Montalvo · 2024 [cited by applicant]
US 20240357367A1 · Montalvo · 2024 [cited by applicant]
US 20240357368A1 · Montalvo · 2024 [cited by applicant]
US 20240357370A1 · Montalvo · 2024 [cited by applicant]
US 20240381101A1 · Montalvo · 2024 [cited by applicant]
US 20240381103A1 · Montalvo · 2024 [cited by applicant]
US 20240388923A1 · Montalvo et al. · 2024 [cited by applicant]
US 20240397332A1 · Montalvo · 2024 [cited by applicant]
US 20240406743A1 · Montalvo et al. · 2024 [cited by applicant]
US 20240406744A1 · Montalvo · 2024 [cited by applicant]
US 20240414547A1 · Montalvo et al. · 2024 [cited by applicant]
US 20240414548A1 · Montalvo · 2024 [cited by applicant]
US 20240414553A1 · Montalvo · 2024 [cited by applicant]
US 20240422555A1 · Montalvo et al. · 2024 [cited by applicant]
US 20240422564A1 · Montalvo · 2024 [cited by applicant]
US 20240430688A1 · Montalvo · 2024 [cited by applicant]
US 20240430689A1 · Montalvo · 2024 [cited by applicant]
US 20240430691A1 · Montalvo · 2024 [cited by applicant]
US 20240430692A1 · Montalvo · 2024 [cited by applicant]
US 20250008337A1 · Montalvo · 2025 [cited by applicant]
US 20250048115A1 · Montalvo · 2025 [cited by applicant]
US 20250056237A1 · Montalvo · 2025 [cited by applicant]
US 20250063373A1 · Montalvo · 2025 [cited by applicant]
US 20250063376A1 · Montalvo · 2025 [cited by applicant]
US 20250063377A1 · Montalvo · 2025 [cited by applicant]
US 20250071558A1 · Montalvo · 2025 [cited by applicant]
US 20250071559A1 · Montalvo · 2025 [cited by applicant]
US 20250071561A1 · Montalvo · 2025 [cited by applicant]
US 20250071562A1 · Montalvo · 2025 [cited by applicant]
US 20250080991A1 · Montalvo · 2025 [cited by applicant]
US 20250106642A1 · Montalvo · 2025 [cited by applicant]
US 20250113202A1 · Montalvo · 2025 [cited by applicant]
US 20250126483A1 · Montalvo · 2025 [cited by applicant]
US 20250133411A1 · Montalvo · 2025 [cited by applicant]
US 20250159488A1 · Montalvo · 2025 [cited by applicant]
US 20250184747A1 · Montalvo · 2025 [cited by applicant]
CN 114173379A · 2022 [cited by applicant]
EP 2538553A1 · 2012 [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]