Localization-Oriented Digital Twinning in 6G: A New Indoor-Positioning Paradigm and Proof-of-Concept

Witnessing its large swaths of success in various fields, digital twins (DTs) are considered a promising scheme for 6th Generation (6G) cellular systems, showing a leading edge in networking and communication modelling. However, another 6G core property of high-precision positioning can hardly be su...

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Bibliographic Details
Published in:IEEE transactions on wireless communications 2024-08, Vol.23 (8), p.10473-10486
Main Authors: Gao, Kaixuan, Wang, Huiqiang, Lv, Hongwu, Liu, Wenxue
Format: Article
Language:English
Subjects:
3GPP
5G mobile communication
6G mobile communication
Accuracy
AI-based positioning
Artificial intelligence
Data acquisition
Digital twin
Digital twins
Environment models
indoor positioning
Localization
Location awareness
Mirrors
Optimization
proof-of-concept
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Summary:Witnessing its large swaths of success in various fields, digital twins (DTs) are considered a promising scheme for 6th Generation (6G) cellular systems, showing a leading edge in networking and communication modelling. However, another 6G core property of high-precision positioning can hardly be supported by existing 6G DT solutions due to the lack of environmental modelling and signal interactions with physical scenes. This shortcoming yields a series of challenges in 6G DT-enabled positioning, including positioning data acquisition, accuracy enhancement, and continuous optimization. In this regard, we propose a novel paradigm of localization-oriented DT (LocDT) with a compound architecture of 7 sub-DT layers to characterize the 6G integrated-localization-and-communication (ILAC) feature. LocDT starts from a physical environment sublayer to mirror 6G signal interactions within a real-world scenario, along with an ILAC baseband sublayer and a channel frequency Polar-coordinate (CFP) image construction method to provide finer-grained fingerprints. Furthermore, insight from LocDT reveals an interesting phenomenon: the channel features of Line-of-Sight (LoS) / None-Los (NLoS) gNodeBs make differentiated-contributions to positioning accuracy, especially in wide-existing partial-LoS-coverage scenarios. Benefiting from this, a DT-driven Artificial Intelligence (AI) positioning model, SSI-Net, is designed with a device-attention mechanism, achieving complementary improvements in accuracy. Evaluation results show LocDT and SSI-Net's advantages from a position-of-strength in accuracy and time overhead, outperforming state-of-the-art models.
ISSN:1536-1276
1558-2248
DOI:10.1109/TWC.2024.3373034