The paper proposes a signature-based approach for global channel charting (CC) with ultra-low complexity. The key highlights are:
Channel impulse response (CIR) is transformed into a compact signature map using an iterated-integral based method called signature transform. This reduces the dimensionality of the CIR features by over 87% without sacrificing performance.
A signature-based principal component analysis (SPCA) is proposed for CC, where the dimensionality of the covariance matrix is very small and only proportional to the number of base stations.
A signature-based Siamese network (SSN) is proposed for CC, which uses a novel distance metric that can faithfully capture both local and global geometry without constructing neighborhood graphs.
Experiments on synthetic and real-world datasets show the proposed methods achieve better performance on both local and global similarity compared to CIR-based approaches, while significantly reducing the computational complexity.
The signature-based approach transforms the raw CIR data into a low-dimensional yet informative feature representation, enabling efficient and accurate channel charting for applications like indoor localization and beam management.
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