Clustering Algorithms and Validation Indices for mmWave Radio Multipath Propagation

Moayyed, Miead Tehrani; Antonescu, Bogdan; Basagni, Stefano

doi:10.1109/wts.2019.8715540

Cited by 9 publications

(5 citation statements)

References 18 publications

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“…The solution to this multi-object identification is clustering. Literature reports various clustering approaches that can be used for this purpose [29]- [31].…”

Section: Challengesmentioning

confidence: 99%

Radar-Based Millimeter-Wave Sensing for Accurate 3-D Indoor Positioning: Potentials and Challenges

Sesyuk,

Ioannou,

Raspopoulos

2024

J. Ind. Sea. Pos. Nav.

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The 3D nature of modern smart applications has imposed significant 3D positioning accuracy requirements, especially in indoor environments. However, a major limitation of most existing indoor localization systems is their focus on estimating positions mainly in the horizontal plane, overlooking the crucial vertical dimension. This neglect presents considerable challenges in accurately determining the 3D position of devices such as drones and individuals across multiple floors of a building let alone the cmlevel accuracy that might be required in many of these applications. To tackle this issue, millimeter-wave (mmWave) positioning systems have emerged as a promising technology offering high accuracy and robustness even in complex indoor environments. This paper aims to leverage the potential of mmWave radar technology to achieve precise ranging and angling measurements presenting a comprehensive methodology for evaluating the performance of mmWave sensors in terms of measurement precision while demonstrating the 3D positioning accuracy that can be achieved. The main challenges and the respective solutions associated with the use of mmWave sensors for indoor positioning are highlighted, providing valuable insights into their potentials and suitability for practical applications.

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“…The solution to this multi-object identification is clustering. Literature reports various clustering approaches that can be used for this purpose [29]- [31].…”

Section: Challengesmentioning

confidence: 99%

Radar-Based Millimeter-Wave Sensing for Accurate 3-D Indoor Positioning: Potentials and Challenges

Sesyuk,

Ioannou,

Raspopoulos

2024

J. Ind. Sea. Pos. Nav.

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“…Meanwhile, the COST 2100 stochastic channel model in [19] produces the clusterwise CSI in terms of UT-level CSI for densely deployed UTs within the scope of the entire area by clustering. Nonetheless, conventional clustering techniques such as the KPowerMeans [20], the k-means [21] and the k-nearest neighbors [22], are not robust enough to imperfect UT-level CSI produced by the deep learning based technique. Fuzzy clustering such as UK-means in [23] and the conventional fuzzy c-means (FCM) clustering algorithm in [24], can be promising solution to combat the weakness of the imperfect UT-level CSI.…”

Section: A Related Workmentioning

confidence: 99%

A Deep Learning and Geospatial Data-Based Channel Estimation Technique for Hybrid Massive MIMO Systems

et al. 2021

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This paper presents a novel channel estimation technique for the multi-user massive multiple-input multiple-output (MU-mMIMO) systems using angular-based hybrid precoding (AB-HP). The proposed channel estimation technique generates group-wise channel state information (CSI) of user terminal (UT) zones in the service area by deep neural networks (DNN) and fuzzy c-Means (FCM) clustering. The slow time-varying CSI between the base station (BS) and feasible UT locations in the service area is calculated from the geospatial data by offline ray tracing and a DNN-based path estimation model associated with the 1-dimensional convolutional neural network (1D-CNN) and regression tree ensembles. Then, the UT-level CSI of all feasible locations is grouped into clusters by a proposed FCM clustering. Finally, the service area is divided into a number of non-overlapping UT zones. Each UT zone is characterized by a corresponding set of clusters named as UT-group CSI, which is utilized in the analog RF beamformer design of AB-HP to reduce the required large online CSI overhead in the MU-mMIMO systems. Then, the reduced-size online CSI is employed in the baseband (BB) precoder of AB-HP. Simulations are conducted in the indoor scenario at 28 GHz and tested in an AB-HP MU-mMIMO system with a uniform rectangular array (URA) having 16 × 16 = 256 antennas and 22 RF chains. Illustrative results indicate that 91.4% online CSI can be reduced by using the proposed offline channel estimation technique as compared to the conventional online channel sounding. The proposed DNN-based path estimation technique produces same amount of UT-level CSI with runtime reduced by 65.8% as compared to the computationally expensive ray tracing. The imperfection of UT-level CSI introduced by the DNN-based path estimation technique is mitigated by the FCM clustering technique, where the AB-HP using offline UT-group CSI generated by the DNN-based channel estimation model and ray tracing based model for the RF beamformer achieves, respectively, 98.7% and 99.1% sum-rate performance of the fully digital precoding (FDP) technique using full-size online CSI.

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“…The first framework for applying an automatic algorithm was reported by [2], [3], where the K-means algorithm was utilized, and the Multipath Component Distance (MCD) was used. The K-means algorithm was also used in an urban scenario reported by [4]. A spectral-based power-weighted algorithm was proposed by [5] and applied to measured data in a hall environment.…”

Section: Introductionmentioning

confidence: 99%

Optimal Clustering of Wireless Multipaths by Uniform Manifold Approximation and Projection-Assisted Dbscan

Trinidad,

Materum

2024

IIUMEJ

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Uniform Manifold Approximation and Projection (UMAP) is applied to reduce the multipath dataset into 2-dimensions (2D) for visualization and clustering. Density-based spatial clustering of applications with noise (DBSCAN) is used as the clustering approach and the performance of different search radius epsilon ?. The proposed approach was used to cluster semi-urban scenarios of the COST2100 channel model (C2CM), which has many multipath components (MPCs). The approach is validated by comparing the clustering results to the ground truth and computing the Adjusted Rand Index (ARI) and the cluster-wise Jaccard index . The results suggest that lowering the search radius up to 0.3 achieved a median below 0.6 in the multiple-links scenarios due to the overlapping nature of clusters. Nevertheless, the median values above 0.7 and 0.8 for the ARI and Jaccard index , respectively for the single-link scenarios indicate the robsutness of the approach. ABSTRAK: Anggaran Manifold Seragam dan Unjuran (UMAP) 2-dimensi (2D) digunakan sebagai penggambaran dan pengelasan bagi mengurangkan set data pelbagai laluan. Aplikasi pengelasan ruangan bersama bunyi berdasarkan ketumpatan (DBSCAN) ini mengguna pakai pendekatan pengelasan dan prestasi pelbagai radius carian epsilon ?. Pendekatan yang dicadangkan ini digunakan bagi pengelasan senario separa-bandar model saluran COST2100 (C2CM), di mana komponen ini mempunyai banyak laluan (MPCs). Pendekatan ini disahkan dengan membandingkan dapatan pengelasan kepada kesahihan lapangan, pengiraan Indeks Rawak Terlaras (ARI) dan indeks Jaccard pengelasan ?. Dapatan menunjukkan pengurangan radius carian sehingga 0.3 dicapai pada median di bawah 0.6 dalam senario pelbagai pautan disebabkan oleh sifat pertindihan pengelasan. Walau bagaimanapun, nilai median di atas 0.7 dan 0.8 untuk ARI dan indeks Jaccard ?, masing-masing menunjukkan kaedah ini berkesan bagi senario pautan-tunggal.

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Clustering Algorithms and Validation Indices for mmWave Radio Multipath Propagation

Cited by 9 publications

References 18 publications

Radar-Based Millimeter-Wave Sensing for Accurate 3-D Indoor Positioning: Potentials and Challenges

Radar-Based Millimeter-Wave Sensing for Accurate 3-D Indoor Positioning: Potentials and Challenges

A Deep Learning and Geospatial Data-Based Channel Estimation Technique for Hybrid Massive MIMO Systems

Optimal Clustering of Wireless Multipaths by Uniform Manifold Approximation and Projection-Assisted Dbscan

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