An Intent Translation Framework for Internet of Things
draft-gu-nmrg-intent-translator-02
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| Document | Type | Active Internet-Draft (individual) | |
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| Authors | Mose Gu , Jaehoon Paul Jeong | ||
| Last updated | 2025-10-20 | ||
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draft-gu-nmrg-intent-translator-02
Internet Research Task Force M. Gu, Ed.
Internet-Draft J. Jeong, Ed.
Intended status: Informational Sungkyunkwan University
Expires: 23 April 2026 20 October 2025
An Intent Translation Framework for Internet of Things
draft-gu-nmrg-intent-translator-02
Abstract
The evolution of 6G networks and the expansion of Internet of Things
(IoT) environments introduce new challenges in managing diverse
networked resources. Intent-based management frameworks enable
operators to express desired network outcomes using high-level
intents, often articulated in natural language. However, converting
these expressions into machine-executable policy configurations
remains an open challenge.
This document defines an intent translation framework designed to
bridge the gap between user-issued intents and structured policy
representations for 6G enabled IoT systems. The framework accepts
natural language intent as input and produces a policy document in a
structured format, such as YAML, that aligns with the intent model
defined in 3GPP in [TS-28.312].
The framework consists of modular components responsible for
processing input, aligning user intent with domain knowledge,
evaluating semantic confidence, and generating standardized output.
This modularity supports transparency, interoperability, and
automated policy enforcement in next-generation network
infrastructures.
Status of This Memo
This Internet-Draft is submitted in full conformance with the
provisions of BCP 78 and BCP 79.
Internet-Drafts are working documents of the Internet Engineering
Task Force (IETF). Note that other groups may also distribute
working documents as Internet-Drafts. The list of current Internet-
Drafts is at https://datatracker.ietf.org/drafts/current/.
Internet-Drafts are draft documents valid for a maximum of six months
and may be updated, replaced, or obsoleted by other documents at any
time. It is inappropriate to use Internet-Drafts as reference
material or to cite them other than as "work in progress."
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This Internet-Draft will expire on 23 April 2026.
Copyright Notice
Copyright (c) 2025 IETF Trust and the persons identified as the
document authors. All rights reserved.
This document is subject to BCP 78 and the IETF Trust's Legal
Provisions Relating to IETF Documents (https://trustee.ietf.org/
license-info) in effect on the date of publication of this document.
Please review these documents carefully, as they describe your rights
and restrictions with respect to this document. Code Components
extracted from this document must include Revised BSD License text as
described in Section 4.e of the Trust Legal Provisions and are
provided without warranty as described in the Revised BSD License.
Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2
2. Terminology . . . . . . . . . . . . . . . . . . . . . . . . . 3
3. Intent Translator Framework Architecture . . . . . . . . . . 4
3.1. Intent Translator . . . . . . . . . . . . . . . . . . . . 5
3.2. Semantic Mapper . . . . . . . . . . . . . . . . . . . . . 7
3.3. Intent Resolver . . . . . . . . . . . . . . . . . . . . . 9
4. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 11
5. Deployment Considerations . . . . . . . . . . . . . . . . . . 11
6. Extensibility Considerations . . . . . . . . . . . . . . . . 11
7. Degradation and Human Oversight Considerations . . . . . . . 12
8. Security Considerations . . . . . . . . . . . . . . . . . . . 12
9. References . . . . . . . . . . . . . . . . . . . . . . . . . 12
9.1. Normative References . . . . . . . . . . . . . . . . . . 12
9.2. Informative References . . . . . . . . . . . . . . . . . 13
Appendix A. Acknowledgments . . . . . . . . . . . . . . . . . . 16
Appendix B. Contributors . . . . . . . . . . . . . . . . . . . . 16
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 16
1. Introduction
The rapid growth of Internet of Things (IoT) deployments and the
evolution toward 6G networks have introduced increasing complexity in
the management of heterogeneous devices, services, and application
policies. As operational environments scale, the traditional model
of manually configuring service-level policies becomes unsustainable.
Intent-based management is a paradigm that allows administrators to
specify desired outcomes through high-level intents, often expressed
in natural language. These intents must then be interpreted,
validated, and translated into structured policy representations that
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can be executed by network functions and orchestrators. While
standards such as 3GPP TS 28.312 define lifecycle procedures for
managing intents in mobile networks, they do not specify mechanisms
for interpreting natural language or translating it into compliant
policy structures.
This document defines a modular intent translation framework that
addresses this gap. The framework enables automated conversion of
user-issued intents into structured policy outputs in formats such as
YAML, aligned with the expectations and procedures defined in 3GPP TS
28.312. It supports a range of use cases across IoT domains,
including resource optimization, security management, and service
quality assurance.
The framework is composed of functional components that operate
sequentially or in coordination:
* Intent Processing Component:Accepts and interprets user-provided
intents into structured representations.
* Semantic Alignment Component:Matches the processed intent to
relevant domain knowledge for policy resolution.
* Confidence Evaluation Component:Assesses interpretation
reliability and identifies degraded or low-confidence mappings.
* Policy Generation Component:Produces a machine-readable policy in
a structured format suitable for deployment.
The design promotes modularity, transparency, and alignment with
existing network automation architectures. It enables consistent
translation of operator goals into policies that are interoperable
with standard orchestration and management systems in IoT
environment.
2. Terminology
This document uses the terminology defined in [RFC9315], [RFC8329],
[I-D.ietf-i2nsf-applicability],
[I-D.jeong-nmrg-security-management-automation], and [SPT].
In addition, the following terms are defined for the purpose of this
document:
* Intent: A set of operational goals (that a network should meet)
and outcomes (that a network is supposed to deliver), defined in a
declarative manner without specifying how to achieve or implement
them [RFC9315].
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* Intent-Based Management (IBM): It enforces an intent from a user
(or administrator) into a target system. An intent can be
expressed as a natural language sentence and translated into a
high-level policy using natural language interpretation and
structured policy mapping
[I-D.jeong-nmrg-security-management-automation], [SPT]. This
high-level policy is then transformed into low-level policies that
are dispatched to appropriate Service Functions (SFs). Based on
monitoring feedback, new rules may be generated or existing
policies refined.
* Intent Processing Component: A logical function that receives a
natural language input from the user or operator and produces a
structured internal representation of the intent. This component
facilitates the initial abstraction required for intent lifecycle
operations [RFC9315],
[I-D.jeong-nmrg-security-management-automation].
* Semantic Alignment Component: A logical function that interprets
the structured intent and determines its best alignment with
existing domain knowledge or policy databases. The purpose of
this component is to ensure that the intent maps to a resolvable
and enforceable policy outcome.
* Confidence Evaluation Component: A logical function that estimates
the reliability or confidence of semantic intent translation.
Low-confidence outputs may be flagged as degraded and subjected to
fallback, verification, or reprocessing.
* Degraded Intent: An intent translation result that has been marked
as low-confidence due to weak alignment, missing information, or
uncertainty in the reasoning process. A degraded intent may still
result in policy generation, but with warnings or limited scope.
* Policy Generation Component: A logical function that produces a
machine-readable policy, typically in a format such as YAML or
JSON, based on the resolved intent and domain mappings. This
component ensures compliance with policy schema requirements, such
as those defined in 3GPP [TS-28.312].
3. Intent Translator Framework Architecture
This section defines the architecture of the Intent Translator
Framework, which is designed to convert high-level, natural language
intents into machine-readable policy representations in structured
formats such as YAML. The framework enables intent-based management
automation for IoT environments and aligns with policy modeling and
lifecycle procedures defined in [TS-28.312].
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3.1. Intent Translator
The Intent Translator is a modular subsystem responsible for
converting natural language intent into a structured, machine-
readable policy document suitable for enforcement in intent-based
management systems. It forms the core of the intent translation
framework, providing semantic interpretation, policy grounding, and
output generation. The final output is expressed in a structured
format such as YAML and adheres to policy modeling defined in
[TS-28.312].
Architecturally, the Intent Translator operates as a sequential
pipeline composed of six logically distinct components. Each
component handles a specific transformation step, beginning with user
input and ending with policy document generation. The pipeline
enables soft semantic matching, confidence scoring, and graceful
handling of degraded translations. It is designed to support
transparent, extensible, and standards-aligned translation of human
goals into actionable configurations.
+----------+
| IBN User |
+----------+
|
|
+------------+------------------------------------------------------------+
| v Intent Translator |
| +--------------------+ +------------------+ |
<+--| Intent Coordinator |-->| Intent Extractor | |
| +--------------------+ +------------------+ |
| ^ | |
| | v |
| | +-----------------+ |
| | | Semantic Mapper | |
| | +-----------------+ |
| | | |
| | v |
| +-----------------+ +-----------------+ +----------------+ |
| | Policy Composer |<-----| Intent Resolver |<----->| Policy | |
| +-----------------+ +-----------------+ | Knowledge Base | |
| +----------------+ |
+-------------------------------------------------------------------------+
Figure 1: Intent Translator Component Architecture
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The six main components of the Intent Translator are illustrated in
Figure 1.
* Intent Coordinator: An initial component of Intent Translator that
receives user intent; dispatches and deploys. This component
receives a natural language intent submitted by an IBN user or
administrative interface. It forwards the natural language intent
to the Intent Extractor. On the other hand, once the Policy
Composer generates the final structured policy, the Intent
Coordinator is also responsible for delivering it to downstream
systems for enforcement, such as network controllers or
orchestration engines.
* Intent Extractor: A Few shot-based Large Language Model (LLM)
informed component that extracts structured elements from anatural
language [Flan-T5][GPT-3]. This component parses the incoming
natural language statement to extract key semantic elements such
as action, expactation object, and expactation target. These
elements form the core of the structured intent representation and
are passed to the Semantic mapper for KG embedding and semantic
alignment.
* Semantic Mapper: A component that projects structured intent into
a semantic space aligned with domain knowledge. This component
maps the structured intent fields into dense vector
representations using a pre-trained embedding space. The
embedding model is aligned with a domain-specific knowledge graph
and allows the intent representation to be projected into the same
semantic space used for stored policy facts. The aggregated
intent vector is delivered to the reasoning module, Intent
Resolver.
* Intent Resolver: A Reasoning module that atches intent to policy
triples; flags degraded matches. This component receives the
embedded intent vector and compares it against the embeddings of
knowledge graph triples stored in the Policy Knowledge Base. If a
direct match is not available, soft matching is performed using
semantic similarity scoring (e.g., cosine distance). When the
similarity score for the best match falls below a defined
threshold, the match is flagged as degraded. This degraded status
is propagated downstream, enabling conditional processing and
transparency in policy output.
* Policy Knowledge Base: A knowledge base component that stores
domain knowledge to provides embeddings and semantic structure.
The Knowledge Base maintains the structured knowledge graph used
throughout the translation process. It contains entity-relation
triples that define valid intent mappings and service
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configurations. During reasoning, the Intent Resolver retrieves
and compares Knowledge Base entries based on their semantic
proximity to the intent vector. The Policy Knowledge Base
supports approximate matching during inference.
* Policy Composer: Generates YAML-formatted policy documents for
deployment. The final component of the translation pipeline is
responsible for synthesizing a policy document that aligns with
[TS-28.312]. It uses both the extracted intent structure and the
selected knowledge graph entry to construct a template based YAML-
formatted policy.
Together, these components enable the reliable and transparent
transformation of user-defined goals into system-aligned, deployable
policies. The architecture is modular and extensible, allowing
domain-specific enhancements without modification to the full
translation pipeline.
3.2. Semantic Mapper
The Semantic Mapper translates structured intent fields into a
unified semantic representation within an embedding space aligned
with the Policy Knowledge Base. It serves as a semantic abstraction
layer that enables approximate intent matching through latent space
projection.
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+------------------+
| Intent Extractor |
+------------------+
|
+-------------------+-------------------+
| | Semantic |
| v Mapper |
| +-----------------------------+ |
| | Embedding Module | |
| | (Per-slot Vector Encoding) | |
| +-----------------------------+ |
| | |
| v |
| +-----------------------------+ |
| | Aggregator | |
| | (Intent Embedding Builder) | |
| +-----------------------------+ |
| | |
| v |
| [ Aggregated Intent Vector ] |
+-------------------+-------------------+
v
+-----------------+ +----------------+
| Intent Resolver |<-------->| Knowledge Base |
+-----------------+ +----------------+
Figure 2: Semantic Mapper Internal Workflow
Figure 2 illustrates the internal flow of the Semantic Mapper. It
begins with a structured intent delivered from the Intent Parser.
The input undergoes semantic enrichment using a few-shot language
model, followed by vector encoding of each semantic slot. The final
stage aggregates the slot-wise vectors into a single intent embedding
vector, which is then forwarded to the Intent Resolver.
The internal components of the Semantic Mapper are illustrated in
Figure 2.
* Embedding Module: A component that supports Per-slot Vector
Encoding. Each enriched intent slots (e.g., action, expaction
object, expectation target) is independently encoded as a dense
vector in a shared semantic space. This allows for flexible
alignment across synonymous or paraphrased expressions.
* Aggregator: A component that builds intent embeddings. This
component uses a simple neural network to learn weights for
individual slots, dynamically reflecting the importance of each
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slot to construct an intent vector, and aggregates it into a
single composite intent embedding. This intent embedding vector
serves as a holistic semantic representation of the user's goal,
facilitating efficient semantic comparisons during the inference
phase.
The final output of the Semantic Mapper is the aggregated intent
vector, which is passed to the Intent Resolver for policy resolution.
This design supports soft matching and generalization over variations
in language, domain vocabulary, and abstraction level.
3.3. Intent Resolver
The Intent Resolver is responsible for mapping the semantic
representation of a user's intent to a corresponding policy concept
within the Policy Knowledge Base. It performs soft matching,
evaluates confidence, and applies a thresholding mechanism to
determine whether to generate a policy or flag the result for
operator intervention.
+-----------------+
| Semantic Mapper |
+-----------------+
+------------------+-----------------------+
| | Intent |
| v Resolver |
| +----------------------+ | +-----------+
| | Soft Matching Engine |<-----------+---->| knowledge |
| +----------------------+ | | Base |
| | | +-----------+
| v | ^
| +----------------------+ | |
| | Confidence Evaluator | | |
| +----------------------+ | |
| | | |
| v | |
| +--------------------------+ | |
| | Threshold Gate | | |
| +--------------------------+ | |
+--------+--------------------+------------+ |
<Forward> <Degrade> +---------------+ |
v +-----|Feedback Logger|--+
+----------+ +---------------+
| Policy |
| Composer |
+----------+
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Figure 3: Intent Resolver Internal Architecture
The internal architecture of the Intent Resolver is shown in
Figure 3.
* Soft Matching Engine: This component receives the aggregated
intent vector from the Semantic Mapper and computes its semantic
similarity against all relevant policy entries stored in the
Policy Knowledge Base. The comparison uses a vector-space
similarity metric (e.g., cosine similarity) to identify the best-
matching policy triple.
* Confidence Evaluator: Once the most relevant policy candidate is
selected, this component evaluates the semantic confidence score
associated with the match. This score quantifies the degree of
alignment between the user's intent and the closest available
domain policy.
* Threshold Gate: The confidence score is evaluated against a
configurable semantic threshold. If the score exceeds the
threshold, the candidate policy is considered a valid match and is
forwarded to the Policy Composer. If the score falls below the
threshold, the intent is flagged as degraded.
* Feedback Logger: For degraded matches, this component logs the
failure for later analysis and may optionally initiate human-in-
the-loop review. If confirmed or corrected, the outcome can be
used to update the Policy Knowledge Base, thus improving future
resolution accuracy.
The Intent Resolver enables explainable, flexible, and confidence-
aware translation of semantic user goals into domain-aligned policy
artifacts. It ensures that all generated outputs are grounded in
interpretable knowledge while also supporting fallback and learning-
based feedback.
The intent and high-level policy artifacts produced by the Intent
Translator Framework can be expressed in standardized data models
such as XML [RFC6020][RFC7950] or YAML [YAML]. These documents can
be delivered to the appropriate management or orchestration systems
via NETCONF [RFC6241], RESTCONF [RFC8040], or REST API [REST]
interfaces for deployment and enforcement.
As described in the modular architecture of the framework, user-
defined natural language intent is processed through a structured
pipeline that includes the Intent Coordinator, Intent Parser,
Semantic Mapper, Intent Resolver, and Policy Composer. The semantic
reasoning within this pipeline is grounded in a domain-specific
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Policy Knowledge Base, enabling soft matching and degraded intent
handling. This design ensures that operational goals - even when
imprecisely expressed - can be semantically aligned to existing
policy capabilities, enhancing automation readiness and
interoperability across IoT environments.
Therefore, this document proposes a practical and extensible
architecture for intent translation in next-generation management
systems. Through this architecture, high-level user goals can be
reliably mapped to structured policy outputs, and network services
can be automatically configured, validated, and adapted. The
framework enables intent-based management to support scalable,
knowledge-grounded automation in complex service domains such as IoT,
vertical-specific networks, and intelligent edge infrastructures.
4. IANA Considerations
This document does not require any IANA actions.
5. Deployment Considerations
The deployment of the Intent Translator Framework requires alignment
between the domain-specific Policy Knowledge Base and the operational
policies supported by the underlying infrastructure. Domain-specific
vocabularies, service models, and operational goals must be encoded
within the knowledge base to ensure accurate semantic reasoning.
Operators should pre-train or validate the semantic embedding space
against realistic intents and policy sets before enabling full
automation. This is particularly important for service domains where
intent ambiguity or synonymy could lead to unintended configurations.
6. Extensibility Considerations
The modular architecture of the framework allows for individual
components - such as the Semantic Mapper or Policy Composer - to be
adapted to different domains, languages, or knowledge graph models.
As such, implementers can substitute the language model used for
prompting, modify the embedding strategy, or replace the output
schema (e.g., YAML, XML) without altering the end-to-end translation
flow.
Additionally, the Policy Knowledge Base may be extended over time
with new policy triples and relations to support evolving service
capabilities. Such extensions should preserve backward compatibility
by maintaining stable identifiers for core operational concepts.
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7. Degradation and Human Oversight Considerations
The framework supports degraded intent resolution via soft matching
and confidence scoring. While this enables flexible operation in the
presence of vocabulary incompleteness or paraphrasing, operators
should evaluate how degraded matches are handled within their intent
lifecycle.
For high-assurance environments, degraded outputs should be reviewed
by a human operator or routed to a validation pipeline before policy
deployment. Logging mechanisms should be used to record degraded
cases and their resolution outcomes to improve future model
performance and policy reliability.
8. Security Considerations
The Intent Translation Framework must operate over authenticated and
confidential channels (e.g., TLS/HTTPS) to prevent eavesdropping,
message tampering, or replay attacks by malicious actors.
Implementations should enforce strict certificate validation and
regularly rotate cryptographic keys to maintain transport-layer
security.
Because the system processes free-form natural language intents, it
is vulnerable to adversarially crafted inputs designed to produce
unintended or harmful policies. Deployments should incorporate input
validation and semantic sanity checks such as confirmation interfaces
for high-impact operations and rate limit or quarantine suspicious
requests for manual review.
Intent collisions where contradictory or overlapping intents are
submitted concurrently can lead to policy conflicts or enforcement
gaps. The framework must include conflict-detection logic in its
Policy Composer component and either automatically resolve detected
collisions using a documented precedence model or escalate them to
human operators for arbitration.
9. References
9.1. Normative References
[RFC6020] Bjorklund, M., Ed., "YANG - A Data Modeling Language for
the Network Configuration Protocol (NETCONF)", RFC 6020,
DOI 10.17487/RFC6020, October 2010,
<https://www.rfc-editor.org/info/rfc6020>.
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[RFC6241] Enns, R., Ed., Bjorklund, M., Ed., Schoenwaelder, J., Ed.,
and A. Bierman, Ed., "Network Configuration Protocol
(NETCONF)", RFC 6241, DOI 10.17487/RFC6241, June 2011,
<https://www.rfc-editor.org/info/rfc6241>.
[RFC7950] Bjorklund, M., Ed., "The YANG 1.1 Data Modeling Language",
RFC 7950, DOI 10.17487/RFC7950, August 2016,
<https://www.rfc-editor.org/info/rfc7950>.
[RFC8040] Bierman, A., Bjorklund, M., and K. Watsen, "RESTCONF
Protocol", RFC 8040, DOI 10.17487/RFC8040, January 2017,
<https://www.rfc-editor.org/info/rfc8040>.
[RFC8329] Lopez, D., Lopez, E., Dunbar, L., Strassner, J., and R.
Kumar, "Framework for Interface to Network Security
Functions", RFC 8329, DOI 10.17487/RFC8329, February 2018,
<https://www.rfc-editor.org/info/rfc8329>.
[RFC9315] Clemm, A., Ciavaglia, L., Granville, L. Z., and J.
Tantsura, "Intent-Based Networking - Concepts and
Definitions", RFC 9315, DOI 10.17487/RFC9315, October
2022, <https://www.rfc-editor.org/info/rfc9315>.
[RFC9365] Jeong, J., Ed., "IPv6 Wireless Access in Vehicular
Environments (IPWAVE): Problem Statement and Use Cases",
RFC 9365, DOI 10.17487/RFC9365, March 2023,
<https://www.rfc-editor.org/info/rfc9365>.
9.2. Informative References
[I-D.ietf-i2nsf-applicability]
Jeong, J. P., Hyun, S., Ahn, T., Hares, S., and D. Lopez,
"Applicability of Interfaces to Network Security Functions
to Network-Based Security Services", Work in Progress,
Internet-Draft, draft-ietf-i2nsf-applicability-19, 3 April
2025, <https://datatracker.ietf.org/doc/html/draft-ietf-
i2nsf-applicability-19>.
[I-D.jeong-nmrg-security-management-automation]
Jeong, J. P., Lingga, P., Park, J., Lopez, D. R., and S.
Hares, "An I2NSF Framework for Security Management
Automation in Cloud-Based Security Systems", Work in
Progress, Internet-Draft, draft-jeong-nmrg-security-
management-automation-00, 20 October 2025,
<https://datatracker.ietf.org/doc/html/draft-jeong-nmrg-
security-management-automation-00>.
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[I-D.jeong-nmrg-ibn-network-management-automation]
Jeong, J. P., Ahn, Y., Gu, M., Kim, Y., and J. Jung-Soo,
"Intent-Based Network Management Automation in 5G
Networks", Work in Progress, Internet-Draft, draft-jeong-
nmrg-ibn-network-management-automation-06, 9 June 2025,
<https://datatracker.ietf.org/doc/html/draft-jeong-nmrg-
ibn-network-management-automation-06>.
[SPT] Lingga, P., Jeong, J., Yang, J., and J. Kim, "SPT:
Security Policy Translator for Network Security Functions
in Cloud-Based Security Services", IEEE Transactions on
Dependable and Secure Computing, Volume 21, Issue 6,
DOI https://doi.org/10.1109/TDSC.2024.3371788, November
2024, <https://doi.org/https://doi.org/10.1109/
TDSC.2024.3371788>.
[YAML] Ingerson, B., Evans, C., and O. Ben-Kiki, "Yet Another
Markup Language (YAML) 1.0",
Available: https://yaml.org/spec/history/2001-05-26.html,
October 2023.
[TS-23.501]
"System Architecture for the 5G System (5GS)", Available:
https://portal.3gpp.org/desktopmodules/Specifications/
SpecificationDetails.aspx?specificationId=3144, September
2023.
[TS-28.312]
"Intent Driven Management Services for Mobile Networks",
Available:
https://portal.3gpp.org/desktopmodules/Specifications/
SpecificationDetails.aspx?specificationId=3554, September
2023.
[TR-28.812]
"Study on Scenarios for Intent Driven Management Services
for Mobile Networks", Available:
https://portal.3gpp.org/desktopmodules/Specifications/
SpecificationDetails.aspx?specificationId=3553, December
2020.
[TS-23.288]
"Architecture Enhancements for 5G System (5GS) to Support
Network Data Analytics Services", Available:
https://portal.3gpp.org/desktopmodules/Specifications/
SpecificationDetails.aspx?specificationId=3579, September
2023.
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[TS-29.520]
"Network Data Analytics Services", Available:
https://portal.3gpp.org/desktopmodules/Specifications/
SpecificationDetails.aspx?specificationId=3355, September
2023.
[RFC7149] Boucadair, M. and C. Jacquenet, "Software-Defined
Networking: A Perspective from within a Service Provider
Environment", RFC 7149, March 2014,
<https://www.rfc-editor.org/rfc/rfc7149>.
[Flan-T5] Chung, H., "Scaling Instruction-Finetuned Language
Models", arXiv arXiv:2210.11416,
Available: https://arxiv.org/abs/2210.11416, October 2022.
[GPT-3] Brown, T., "Language Models are Few-Shot Learners",
arXiv arXiv:2005.14165,
Available: https://arxiv.org/abs/2005.14165, May 2020.
[USENIX-ATC-Lumi]
Jacobs, A., Pfitscher, R., Ribeiro, R., Ferreira, R.,
Granville, L., Willinger, W., and S. Rao, "Hey, Lumi!
Using Natural Language for Intent-Based Network
Management", USENIX Annual Technical Conference,
Available:
https://www.usenix.org/conference/atc21/presentation/
jacobs, July 2021.
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Appendix A. Acknowledgments
This work was supported by Institute of Information & Communications
Technology Planning & Evaluation (IITP) grant funded by the Korea
Ministry of Science and ICT (MSIT) (No. RS-2024-00398199 and RS-
2022-II221015).
This work was supported in part by Institute of Information &
Communications Technology Planning & Evaluation (IITP) grant funded
by the Korea Ministry of Science and ICT (MSIT) (No. 2025-RS-
2022-II221199, Regional strategic industry convergence security core
talent training business).
Appendix B. Contributors
This document is made by the group effort of OPWAWG, greatly
benefiting from inputs and texts by Linda Dunbar (Futurewei) Yong-
Geun Hong (Daejeon University), and Joo-Sang Youn (Dong-Eui
University). The authors sincerely appreciate their contributions.
The following are coauthors of this document:
Yoseop Ahn
Department of Computer Science & Engineering
Sungkyunkwan University
2066 Seobu-Ro, Jangan-Gu
Suwon
Gyeonggi-Do
16419
Republic of Korea
Phone: +82 31 299 4106
Email: ahnjs124@skku.edu
URI: http://iotlab.skku.edu/people-Ahn-Yoseop.php
Authors' Addresses
Mose Gu (editor)
Department of Computer Science and Engineering
2066 Seobu-Ro, Jangan-Gu
Suwon
Gyeonggi-Do
16419
Republic of Korea
Email: rna0415@skku.edu
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Jaehoon Paul Jeong (editor)
Department of Computer Science and Engineering
2066 Seobu-Ro, Jangan-Gu
Suwon
Gyeonggi-Do
16419
Republic of Korea
Phone: +82 31 299 4957
Email: pauljeong@skku.edu
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