Tensor-Based Near-Field Localization Using Massive Antenna Arrays

Podkurkov, Ivan; Seidl, Gabriel; Khamidullina, Liana; Nadeev, Adel; Haardt, Martin

doi:10.1109/tsp.2021.3105797

Cited by 15 publications

(4 citation statements)

References 36 publications

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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

Inventions

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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%

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

Ashaev,

Safiullin,

Gaysin

et al. 2023

Inventions

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“…The ELAA deploys enormous antennas, significantly increasing the array aperture [5]- [8]. The radiative fields of the array contain the near field and far field, and the boundary between the two fields is Rayleigh distance, defined as 2D 2 λ W. Li, H. Yin and Z. Qin are with Huazhong University of Science and Technology, 430074 Wuhan, China (e-mail: weidongli@hust.edu.cn, yin@hust.edu.cn, ziao qin@hust.edu.cn).…”

Section: Introductionmentioning

confidence: 99%

A Multi-Dimensional Matrix Pencil-Based Channel Prediction Method for Massive MIMO With Mobility

Yin

Qin

et al. 2023

IEEE Trans. Wireless Commun.

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This paper addresses the mobility problem in massive multiple-input multiple-output systems, which leads to significant performance losses in the practical deployment of the fifth generation mobile communication networks. We propose a novel channel prediction method based on multi-dimensional matrix pencil (MDMP), which estimates the path parameters by exploiting the angular-frequency-domain and angular-timedomain structures of the wideband channel. The MDMP method also entails a novel path pairing scheme to pair the delay and Doppler, based on the super-resolution property of the angle estimation. Our method is able to deal with the realistic constraint of time-varying path delays introduced by user movements, which has not been considered so far in the literature. We prove theoretically that in the scenario with time-varying path delays, the prediction error converges to zero with the increasing number of the base station (BS) antennas, providing that only two arbitrary channel samples are known. We also derive a lower-bound of the number of the BS antennas to achieve a satisfactory performance. Simulation results under the industrial channel model of 3GPP demonstrate that our proposed MDMP method approaches the performance of the stationary scenario even when the users' velocity reaches 120 km/h and the latency of the channel state information is as large as 16 ms.

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“…Although this approximation can simplify the system model, it introduces systematic errors, resulting in reduced estimation accuracy. Using the accurate spherical wavefront model, tensor-based NF source localization algorithms were proposed in [13] and [14], by extracting the angle and range parameters from the estimates of the steering matrices, with improved estimation accuracy as compared to Fresnel approximation adopted in [8,9]. In [15], the conditional and unconditional Cramer-Rao bounds (CRBs) for NF source parameter estimation are analyzed based on the exact spherical wavefront model for bistatic MIMO radar systems.…”

Section: Introductionmentioning

confidence: 99%

“…However, the methods proposed in [21][22][23][24] are mainly focused on FF sources, while existing NF MIMO radar localization methods in [8,9,13,14] ignore variation of the received signal's amplitude from sensor to sensor; as shown in [25][26][27][28][29][30], the attenuation of the signal's amplitude is inversely proportional to the source-sensor distance. In addition, each concentered orthogonal loop and dipole (COLD) antenna consists of a pair of spatially co-located but orthogonal magnetic loop and electric dipole [31].…”

Section: Introductionmentioning

confidence: 99%

An Exact Near-Field Model Based Localization for Bistatic MIMO Radar With COLD Arrays

Chen

Wang

Liu

et al. 2023

IEEE Trans. Veh. Technol.

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Most existing near-field (NF) source localization algorithms are developed based on the Fresnel approximation model, and assume that the spatial amplitudes of the target at the sensors are equal. Unlike these algorithms, an NF source parameter estimation algorithm is proposed, based on the exact spatial propagation geometry model, for bistatic multiple-input multiple-output (MIMO) radar deployed with a linear concentered orthogonal loop and dipole (COLD) array at both the transmitter and receiver. The proposed method first compresses the output signal of the matched filter at the receiver into a third-order parallel factor (PARAFAC) data model, on which a trilinear decomposition is performed, and subsequently three factor matrices can be obtained. Then, multiple parameters of interest, including direction-of-departure (DOD), direction-ofarrival (DOA), range from transmitter to target (RFTT), range from target to receiver (RFTR), two-dimensional (2-D) transmit polarization angle (TPA) and 2-D receive polarization angle (R-PA), are estimated from the spatial amplitude ratio exploiting the rotation invariant property and the Khatri-Rao product. Finally, the phase uncertainties of transmit and receive arrays can be extracted from additional phase items. The proposed algorithm avoids spectrum peak search, and the estimated parameters in closed forms can be automatically matched unambiguously. In addition, it is suitable for non-uniform linear arrays (NLA) with arbitrary array element spacing and phase uncertainty. Advantages of the proposed method are demonstrated by simulation results.

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Tensor-Based Near-Field Localization Using Massive Antenna Arrays

Cited by 15 publications

References 36 publications

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

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

A Multi-Dimensional Matrix Pencil-Based Channel Prediction Method for Massive MIMO With Mobility

An Exact Near-Field Model Based Localization for Bistatic MIMO Radar With COLD Arrays

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