Research and Design of a Large Model for Location-Based Service Awareness and Feature Extraction Based on a Mobile 5G Single Station

NIE Wendi; ZENG Lingxin; WEI Min; WANG Hao; DUAN Yaoxin; ZHAN Cheng; YANG Yang

doi:10.11999/JEIT260854

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NIE Wendi, ZENG Lingxin, WEI Min, WANG Hao, DUAN Yaoxin, ZHAN Cheng, YANG Yang. Research and Design of a Large Model for Location-Based Service Awareness and Feature Extraction Based on a Mobile 5G Single Station[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260854

Citation:

NIE Wendi, ZENG Lingxin, WEI Min, WANG Hao, DUAN Yaoxin, ZHAN Cheng, YANG Yang. Research and Design of a Large Model for Location-Based Service Awareness and Feature Extraction Based on a Mobile 5G Single Station[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260854

Citation:

NIE Wendi, ZENG Lingxin, WEI Min, WANG Hao, DUAN Yaoxin, ZHAN Cheng, YANG Yang. Research and Design of a Large Model for Location-Based Service Awareness and Feature Extraction Based on a Mobile 5G Single Station[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260854

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Research and Design of a Large Model for Location-Based Service Awareness and Feature Extraction Based on a Mobile 5G Single Station

doi: 10.11999/JEIT260854 cstr: 32379.14.JEIT260854

NIE Wendi^1
,,
ZENG Lingxin^1
,,
WEI Min^{1
,
,},
WANG Hao^1
,,
DUAN Yaoxin^1
,,
ZHAN Cheng^2
,,
YANG Yang^2
,

1.
School of Automation / School of Industrial Internet, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2.
School of Computer and Information Science/School of Software, Southwest University, Chongqing 400715, China

Funds: National Science and Technology Major Project(2026ZD1610100), The Natural Science Foundation of Chongqing(CSTB2023NSCQ-LZX0123), The Natural Science Foundation of Chongqing (CSTB2023NSCQ-LZX0075), Open Project of State Key Laboratory of Synthetical Automation for Process Industries (SAPI-2025-KFKT-04)

Accepted Date: 2026-11-05
Rev Recd Date: 2026-11-05

Available Online: 2026-11-14

Abstract

Abstract

Objective The growing demand for high-precision localization in complex indoor scenarios such as industrial warehouses and smart buildings has made 5G integrated sensing and communication (ISAC) a promising enabling technology. However, existing 5G localization schemes are predominantly designed for multi-station cooperation or fixed single-station deployment. Multi-station approaches rely heavily on dense base station deployment, which fails to work in infrastructure-sparse scenarios. Fixed single-station solutions suffer from severe performance degradation in non-line-of-sight (NLOS) and multipath environments due to the limited observation perspective, and conventional fingerprint localization algorithms have limited generalization ability for unknown positions. To address these limitations, this paper proposes a mobile 5G single-station localization framework based on large models, which enables passive localization without active terminal cooperation. Methods A mobile 5G single-station sensing prototype is developed based on the OpenAirInterface (OAI) platform, USRP B210 software-defined radio and automated guided vehicle (AGV) platform (Fig. 1). By reusing the native channel state information reference signal (CSI-RS) measurement and CSI Report reporting mechanism in 5G NR, CSI reports are parsed at the gNB side and reference signal received power (RSRP) sequences are extracted to construct a dedicated localization fingerprint dataset, without introducing additional dedicated sensing signals (Fig. 2). A specialized localization large model named LLM4CSI-RS is built upon the Llama-3.2-3B-Instruct backbone. Parameter-efficient fine-tuning (PEFT) is implemented via low-rank adaptation (LoRA), where only the low-rank matrices in attention projection layers are updated while the pre-trained weights remain frozen, significantly reducing training overhead and mitigating overfitting (Table 1). During online inference, the model outputs confidence scores of candidate reference points, and the final position is estimated through a Top-K weighted fusion strategy. Results and Discussions Extensive experiments are conducted in a real-world indoor NLOS scenario (Fig. 6). The proposed LLM4CSI-RS method achieves a mean localization error of 1.25 m, a 95th percentile error of 2.45 m, and an accuracy of 86.06% within the 2 m error threshold, with the steepest rise in cumulative probability in the low-error range (Fig. 7). Systematic hyperparameter tuning confirms that the baseline low-rank adaptation configuration yields the optimal performance for this task (Table 2). Ablation studies validate the effectiveness of each key module, and the full configuration — with low-rank adaptation applied to all attention projection matrices, a single-layer linear classifier, global mean pooling and Top-4 weighted fusion — delivers the best overall performance (Table 3). Comparative experiments across multiple backbone models show that Llama-3.2-3B strikes the optimal balance between feature representation capability and computational cost, avoiding underfitting in smaller models and overfitting in larger ones (Fig. 8). Compared with conventional deep learning baselines including CNN, DNN and LSTM, the proposed method consistently outperforms all benchmarks (Fig. 9). Conclusions The proposed framework integrates the multi-perspective dynamic sensing capability of mobile single stations and the deep sequence modeling ability of large models, effectively alleviates the performance degradation caused by NLOS and sparse infrastructure, and significantly improves localization accuracy and robustness. It provides a low-deployment-cost and scalable technical path for indoor localization in complex scenarios, and offers a practical reference for industrial-grade indoor location-based services.
- Mobile 5G Single Station,
- Fingerprint-based Localization,
- Indoor Localization,
- Large Model Localization Method

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References(33)

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