Accelerating Radio Spectrum Regulation Workflows with Large Language Models (LLMs)
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917622789963776 |
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| author | Ghasemi, Amir Guinand, Paul |
| author_facet | Ghasemi, Amir Guinand, Paul |
| contents | Wireless spectrum regulation is a complex and demanding process due to the rapid pace of technological progress, increasing demand for spectrum, and a multitude of stakeholders with potentially conflicting interests, alongside significant economic implications. To navigate this, regulators must engage effectively with all parties, keep pace with global technology trends, conduct technical evaluations, issue licenses in a timely manner, and comply with various legal and policy frameworks.
In light of these challenges, this paper demonstrates example applications of Large Language Models (LLMs) to expedite spectrum regulatory processes. We explore various roles that LLMs can play in this context while identifying some of the challenges to address. The paper also offers practical case studies and insights, with appropriate experiments, highlighting the transformative potential of LLMs in spectrum management. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_17819 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Accelerating Radio Spectrum Regulation Workflows with Large Language Models (LLMs) Ghasemi, Amir Guinand, Paul Networking and Internet Architecture Artificial Intelligence Wireless spectrum regulation is a complex and demanding process due to the rapid pace of technological progress, increasing demand for spectrum, and a multitude of stakeholders with potentially conflicting interests, alongside significant economic implications. To navigate this, regulators must engage effectively with all parties, keep pace with global technology trends, conduct technical evaluations, issue licenses in a timely manner, and comply with various legal and policy frameworks. In light of these challenges, this paper demonstrates example applications of Large Language Models (LLMs) to expedite spectrum regulatory processes. We explore various roles that LLMs can play in this context while identifying some of the challenges to address. The paper also offers practical case studies and insights, with appropriate experiments, highlighting the transformative potential of LLMs in spectrum management. |
| title | Accelerating Radio Spectrum Regulation Workflows with Large Language Models (LLMs) |
| topic | Networking and Internet Architecture Artificial Intelligence |
| url | https://arxiv.org/abs/2403.17819 |