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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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GeoNR-PSW: Prompt-Aligned Localization Leveraging Ray-Traced 5G Channels and LLM Reasoning.

Wenbin Shi1,2, Zhongxu Zhan2, Jingsheng Lei2

  • 1School of Computer Science, Hangzhou Dianzi University, Hangzhou 310018, China.

Sensors (Basel, Switzerland)
|September 13, 2025
PubMed
Summary
This summary is machine-generated.

GeoNR-PSW offers accurate 5G positioning using pseudo-signal-words and GPT-2 models. This novel architecture achieves precise localization in challenging environments with minimal data, enhancing 5G New Radio (NR) network reliability.

Keywords:
5G NR6Gfew-shot learninglarge language modelslocalizationpseudo-signal words

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Area of Science:

  • Wireless communication
  • Localization and positioning technologies
  • Machine learning for signal processing

Background:

  • Global Navigation Satellite System (GNSS) reliability is often compromised in dense urban and vehicular environments due to multipath effects and signal blockage.
  • Accurate user-equipment positioning is essential for the deployment and performance of 5G New Radio (NR) networks.

Purpose of the Study:

  • To propose GeoNR-PSW, a novel localization architecture for 5G NR networks.
  • To leverage the pseudo-signal-word (PSW) paradigm for 3D localization using 5G channel fingerprints.
  • To enable few-shot learning for 5G positioning with minimal supervision.

Main Methods:

  • Developed the GeoNR-PSW architecture encoding 5G channel snapshots into concise PSW sequences.
  • Utilized a frozen GPT-2 backbone enhanced with lightweight PSW-Adapters for localization.
  • Applied the method to the Raymobtime S007 dataset for sub-6 GHz (FR1) and mmWave (FR2) frequencies.

Main Results:

  • Achieved median localization errors of 5.90 m at FR1 (2.8 GHz) and 3.25 m at FR2 (60 GHz).
  • Demonstrated effective few-shot 3D localization capabilities despite a limited dataset size.
  • Validated the performance of PSWs and prompt-aligned language models for 5G positioning.

Conclusions:

  • GeoNR-PSW shows significant potential for accurate and scalable 5G positioning.
  • Prompt-aligned language models offer a promising approach for 5G localization with minimal supervision.
  • The PSW paradigm effectively enhances positioning accuracy in challenging wireless environments.