Tensor decomposition‐based 3D positioning with a single‐antenna receiver in 5G millimetre wave systems

Dan, Zesheng; Lian, Baowang; Tang, Chengkai; Xu, Hao

doi:10.1049/iet-com.2019.1254

Cited by 1 publication

(2 citation statements)

References 28 publications

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“…On the one hand, ref. [18] presents a 3D positioning technique from the UE perspective. The core idea is to utilize massive MIMO systems in the BS, which allows the UE to realize high-precision positioning using a single antenna.…”

Section: User Mobility Patternmentioning

confidence: 99%

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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: User Mobility Patternmentioning

confidence: 99%

“…In contrast to [18,19], our proposed algorithm analyzes the user mobility rather than the user localization. The user mobility issues is explored in [20,21], where the modeling of user mobility in wireless communication is provided.…”

Section: User Mobility Patternmentioning

confidence: 99%