Robust Multi-Dimensional Model Order Estimation Using LineAr Regression of Global Eigenvalues (LaRGE)

Korobkov, Alexey A.; Diugurova, Marina K.; Haueisen, Jens; Haardt, Martin

doi:10.1109/tsp.2022.3222737

Cited by 2 publications

(1 citation statement)

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“…D) consist of R independent vectors that allow to separate and physically interpret the extracted components. This feature of the CP model is widely used in different areas like signal array processing and signal separation, as well as for MIMO systems [43] and biomedical applications [44]. Moreover, the CP decomposition allows to decrease the volume of the data that is a crucial point for our method of user mobility classifying, since the using of raw multi-linear data for considered problem is not suitable.…”

Section: The Basis Of Tensor Algebramentioning

confidence: 99%

An Approach for Using a Tensor-Based Method for Mobility-User Pattern Determining

Ashaev,

Safiullin,

Gaysin

et al. 2023

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Modern mobile networks exhibit a complex heterogeneous structure. To enhance the Quality of Service (QoS) in these networks, intelligent control mechanisms should be implemented. These functions are based on the processing of large amounts of data and feature extraction. One such feature is information about user mobility. However, directly determining user mobility remains challenging. To address this issue, this study proposes an approach based on multi-linear data processing. The user mobility is proposed to determine, using the multi-linear data, about the changing of the Signal-to-Interference-plus-Noise-Ratio (SINR). SINR varies individually for each user over time, relative to the network’s base stations. It is natural to represent these data as a tensor. A tensor-based preprocessing step employing Canonical Polyadic Decomposition (CPD) is proposed to extract user mobility information and reduce the data volume. In the next step, using the DBSCAN algorithm, users are clustered according to their mobility patterns. Subsequently, users are clustered based on their mobility patterns using the DBSCAN algorithm. The proposed approach is evaluated utilizing data from Network Simulator 3 (NS-3), which simulates a portion of the mobile network. The results of processing these data using the proposed method demonstrate superior performance in determining user mobility.

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Section: The Basis Of Tensor Algebramentioning

confidence: 99%