Hierarchical Network Authentication With Machine-Learned RF Signatures
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Solution Overview
Problem
Existing wireless communication systems face challenges in efficiently managing network authentication in diverse environments, particularly in 5G and beyond networks, where seamless connectivity and robustness are crucial, especially in licensed and unlicensed spectrums, and there is a need for adaptive and hierarchical authentication frameworks to enhance security and efficiency.
Innovation Solution
A hierarchical network authentication framework is introduced, utilizing cryptography-based methods and adaptive authentication protocols to manage network access and security in 5G and beyond networks, including 3GPP LTE, LTE-A, 5G NR, and unlicensed spectrum networks like NR-U and Wi-Fi, ensuring secure and efficient connectivity across various devices and environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional centralized authentication frameworks are used in 5G networks, then network security can be maintained through centralized control, but network complexity and authentication latency increase
Solution Approach 1:
The patent divides the centralized authentication framework into hierarchical levels (core network authentication and access network authentication). The core network performs initial authentication while access networks perform local verification, segmenting the authentication process to reduce complexity and latency while maintaining security through distributed verification.
Solution Approach 2:
The patent introduces a spatial dimension to authentication by enabling access networks to independently verify user equipment without always contacting the core network. This dimensional change allows local authentication decisions, reducing authentication latency and core network complexity while maintaining security through hierarchical verification.
2Reliability
If adaptive authentication mechanisms are implemented to handle diverse network conditions, then network robustness improves, but authentication protocol complexity increases
Solution Approach 1:
The patent implements dynamic authentication where access networks can adaptively select between contacting the core network for authentication or performing local verification based on network conditions, user equipment type, and service requirements. This dynamic approach enhances robustness while managing protocol complexity through context-aware decision-making.
Solution Approach 2:
The patent changes authentication parameters dynamically based on network conditions, allowing access networks to adjust authentication strictness, verification methods, and core network interaction frequency. This parameter adaptation improves network robustness across diverse conditions while managing protocol complexity through flexible configuration.
3Adaptability or versatility
If hierarchical authentication is deployed across licensed and unlicensed spectrums, then seamless connectivity is achieved, but network overhead increases
Solution Approach 1:
The patent performs preliminary authentication in the core network before handover between licensed and unlicensed spectrums. User equipment is pre-authenticated and authorized, allowing access networks to perform rapid local verification during handover without initiating new full authentication sequences, thereby reducing network overhead while enabling seamless connectivity.
Solution Approach 2:
The patent uses the access network as an intermediary that maintains local authentication context and can verify user equipment independently of the core network during spectrum handovers. This intermediary role reduces repeated authentication signaling overhead while ensuring seamless connectivity across licensed and unlicensed spectrums through local verification.
Data Source
AI summary
A non-transitory computer-readable storage medium stores instructions to configure a base station for user equipment (UE) authentication in a wireless network and to cause the base station to perform an operation comprising decoding configuration signaling received from a PHY security function (PSF) of the wireless network. The configuration signaling includes a request for collection of a plurality of signal samples from the UE, the UE authenticated based on successful completion of a first authentication process. A response message is encoded for transmission to the PSF. The response message includes the plurality of UE signal samples. A trained machine learning model received from the PSF is decoded. The trained machine learning model associates the authenticated UE with an RF signature of the UE. The RF signature is based on the plurality of signal samples. A second authentication process of the UE is performed based on the trained model.