Localization-Oriented Digital Twinning in 6G: A New Indoor-Positioning Paradigm and Proof-of-Concept
Kaixuan Gao, Huiqiang Wang, Hongwu Lv, Wenxue Liu
Abstract
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 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">high-precision positioning</i> can hardly be supported by existing 6G DT solutions due to the lack of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">environmental modelling</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">signal interactions with physical scenes</i> . 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 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">differentiated-contributions</i> to positioning accuracy, especially in wide-existing <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">partial-LoS-coverage</i> 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.