IP Library Granted Patent US 12,726,340
Granted Patent B2
US 12,726,340 · App. 18/426,788 · Granted Sep 1, 2026

Selectable encryption for 5G open radio access network

Inventor: Bassem Abi-Farah (Littleton, CO)
Assignee: Boost SubscriberCo L.L.C.
H04L9/0852H04L63/1416H04L63/1425
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 12,726,340
App. No.
18/426,788
Granted
Sep 1, 2026
Kind
B2
Abstract

Techniques for encrypting data within a 5G Open Radio Access Network (O-RAN) includes receiving, at a first module of the 5G O-RAN, a first set of one or more data packets encrypted using mathematical encryption. The method also includes determining, using a machine-learning model trained to detect cybersecurity threats, the existence of a cybersecurity threat associated with the voice or data transaction, and in response, determining to switch encryption from the mathematical encryption to quantum encryption. The method further includes encrypting the one or more data packets using a quantum encryption key to generate quantum-encrypted data packets, transmitting the quantum encryption key from the first module of the 5G O-RAN core to a second module of the 5G O-RAN over a quantum key distribution (QKD) channel, and transmitting the quantum-encrypted data packets from the first module of the 5G O-RAN to the second module of the 5G O-RAN.

Claims (48)

1 . A method of encrypting data within a 5G Open Radio Access Network (O-RAN), the method comprising:

receiving, at a first module of the 5G O-RAN, a first set of one or more data packets pertaining to a voice or data transaction associated with the 5G O-RAN, the first set of one or more data packets being encrypted using mathematical encryption;

determining, based on the first set of one or more data packets and using a machine-learning model trained to detect cybersecurity threats, an existence of a cybersecurity threat associated with the voice or data transaction;

in response to determining the existence of the cybersecurity threat associated with the voice or data transaction based on the first set of one or more data packets, determining to switch encryption of the one or more data packets from the mathematical encryption to quantum encryption;

encrypting the one or more data packets using a quantum encryption key to generate quantum-encrypted data packets;

transmitting the quantum encryption key from the first module of the 5G O-RAN to a second module of the 5G O-RAN over a quantum key distribution (QKD) channel; and

transmitting the quantum-encrypted data packets from the first module of the 5G O-RAN to the second module of the 5G O-RAN.

2 . The method of claim 1 , comprising:

determining that the cybersecurity threat has been addressed; and

responsive to determining that the cybersecurity threat has been addressed, determining to switch from the quantum encryption to the mathematical encryption.

3 . The method of claim 1 , wherein the first module is a cloud-deployed module of the 5G O-RAN core.

4 . The method of claim 1 , wherein the first module is one of: an authentication server function (AUSF) module, a secure anchor function (SEAF) module, an access and mobility management function (AMF) module or a non-3GPP interworking function (N 3 IWF) module of a core of the 5G O-RAN.

5 . The method of claim 1 , wherein the second module is one of: a secure anchor function (SEAF) module, an access and mobility management function (AMF) module, a non-3GPP interworking function (N 3 IWF) module of a core of the 5G O-RAN, or a g-NodeB (gNB) of the 5G O-RAN.

6 . The method of claim 1 , wherein the machine-learning model is a deep learning model configured to detect cyber security threats based on data representing an input set of one or more data packets.

7 . The method of claim 1 , wherein the QKD channel is a fiber-optic channel.

8 . The method of claim 1 , wherein the quantum-encrypted data packets are transmitted from the first module of the 5G O-RAN to the second module of the 5G O-RAN over the QKD channel.

9 . The method of claim 1 , comprising:

decrypting the first set of one or more data packets using mathematical decryption, wherein encrypting the one or more data packets using the quantum encryption key to generate the quantum-encrypted data packets comprises:

encrypting a decrypted version of the one or more data packets after decrypting the first set of one or more data packets using mathematical decryption.

10 . A system of encrypting data within a 5G Open Radio Access Network (O-RAN), the system comprising:

memory encoded with machine-readable instructions; and

one or more processors coupled to the memory, and configured to execute the machine-readable instructions, which when executed, cause the one or more processors to execute operations comprising:

receiving, at a first module of the 5G O-RAN, a first set of one or more data packets pertaining to a voice or data transaction associated with the 5G O-RAN, the first set of one or more data packets being encrypted using mathematical encryption,

determining, based on the first set of one or more data packets and using a machine-learning model trained to detect cybersecurity threats, an existence of a cybersecurity threat associated with the voice or data transaction,

in response to determining the existence of the cybersecurity threat associated with the voice or data transaction based on the first set of one or more data packets, determining to switch encryption of the one or more data packets from the mathematical encryption to quantum encryption,

encrypting the one or more data packets using a quantum encryption key to generate quantum-encrypted data packets,

transmitting the quantum encryption key from the first module of the 5G O-RAN to a second module of the 5G O-RAN over a quantum key distribution (QKD) channel, and

transmitting the quantum-encrypted data packets from the first module of the 5G O-RAN to the second module of the 5G O-RAN.

11 . The system of claim 10 , wherein the operations comprise:

determining that the cybersecurity threat has been addressed; and

responsive to determining that the cybersecurity threat has been addressed, determining to switch from the quantum encryption to the mathematical encryption.

12 . The system of claim 10 , wherein the first module is a cloud-deployed module of a core of the 5G O-RAN.

13 . The system of claim 10 , wherein the first module is one of: an authentication server function (AUSF) module, a secure anchor function (SEAF) module, an access and mobility management function (AMF) module or a non-3GPP interworking function (N3IWF) module of a core of the 5G O-RAN.

14 . The system of claim 10 , wherein the second module is one of: a secure anchor function (SEAF) module, an access and mobility management function (AMF) module, a non-3GPP interworking function (N 3 IWF) module of the 5G O-RAN core, or a g-NodeB (gNB) of the 5G O-RAN.

15 . The system of claim 10 , wherein the machine-learning model is a deep learning model configured to detect cyber security threats based on data representing an input set of one or more data packets.

16 . The system of claim 10 , wherein the quantum-encrypted data packets are transmitted from the first module of the 5G O-RAN to the second module of the 5G O-RAN over the QKD channel.

17 . At least one non-transitory machine-readable storage device encoded with machine-readable instructions, which when executed, cause one or more processing devices to execute operations comprising:

receiving, at a first module of a 5G Open Radio Access Network (O-RAN), a first set of one or more data packets pertaining to a voice or data transaction associated with the 5G O-RAN, the first set of one or more data packets being encrypted using mathematical encryption,

determining, based on the first set of one or more data packets and using a machine-learning model trained to detect cybersecurity threats, an existence of a cybersecurity threat associated with the voice or data transaction,

in response to determining the existence of the cybersecurity threat associated with the voice or data transaction based on the first set of one or more data packets, determining to switch encryption of the one or more data packets from the mathematical encryption to quantum encryption,

encrypting the one or more data packets using a quantum encryption key to generate quantum-encrypted data packets,

transmitting the quantum encryption key from the first module of the 5G O-RAN to a second module of the 5G O-RAN over a quantum key distribution (QKD) channel, and

transmitting the quantum-encrypted data packets from the first module of the 5G O-RAN to the second module of the 5G O-RAN.

18 . The non-transitory machine-readable storage device of claim 17 , wherein the operations comprise:

determining that the cybersecurity threat has been addressed; and

responsive to determining that the cybersecurity threat has been addressed, determining to switch from the quantum encryption to the mathematical encryption.

19 . The non-transitory machine-readable storage device of claim 17 , wherein the first module is one of: an authentication server function (AUSF) module, a secure anchor function (SEAF) module, an access and mobility management function (AMF) module or a non-3GPP interworking function (N3IWF) module of a core of the 5G O-RAN, and the second module is one of: a secure anchor function (SEAF) module, an access and mobility management function (AMF) module, a non-3GPP interworking function (N3IWF) module of a core of the 5G O-RAN, or a g-NodeB (gNB) of the 5G O-RAN.

20 . The non-transitory machine-readable storage device of claim 17 , wherein the quantum-encrypted data packets are transmitted from the first module of the 5G O-RAN to the second module of the 5G O-RAN over the QKD channel.

Assignments (2)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Oct 10, 2025
From: DISH WIRELESS L.L.C.
To: BOOST SUBSCRIBERCO L.L.C.
Reel/Frame 073066/0001 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Mar 6, 2024
From: ABI-FARAH, BASSEM
To: DISH WIRELESS L.L.C.
Reel/Frame 066674/0101 →
Continuity (1)
Related Publication 20250247216A1 · Jul 31, 2025
References Cited (16)
US 12073300B2 · Arbajian · 2024 [cited by examiner]
US 20050198490A1 · Jaganathan · 2005 [cited by examiner]
US 20110016513A1 · Bailey, Jr. · 2011 [cited by examiner]
US 20110182428A1 · Zhao · 2011 [cited by examiner]
US 20170171170A1 · Sun · 2017 [cited by examiner]
US 20180254895A1 · Castinado · 2018 [cited by examiner]
US 20210045193A1 · Mishra · 2021 [cited by examiner]
US 20210306145A1 · Krauthamer et al. · 2021 [cited by applicant]
US 20230099688A1 · Ries · 2023 [cited by examiner]
US 20240113869A1 · Trost · 2024 [cited by examiner]
US 20240305660A1 · Albero · 2024 [cited by examiner]
US 20250150466A1 · Miles · 2025 [cited by examiner]
Adnan et al., “Quantum Key Distribution for 5G Networks: A Review, State of Art and Future Directions, ” Future Internet, Feb. 2022, 14(3):73, 28 pages. [cited by applicant]
Alves et al., “Machine Learning Applied to Anomaly Detection on 5G O-RAN Architecture,” Procedia Computer Science, Jan. 2023, 222:81-93. [cited by applicant]
International Search Report and Written Opinion in International Appln. No. PCT/US2025/012377, mailed on Oct. 23, 2025, 16 pages. [cited by applicant]
Mehic et al., “Quantum Cryptography in 5G Networks: A Comprehensive Overview,” IEEE Communications Surveys & Tutorials, Aug. 2023, 26(1):302-346. [cited by applicant]