Static Clusters Research Articles

Tscluster: A python package for the optimal temporal clustering framework

Temporal clustering extends the conventional task of data clustering by grouping time series data according to shared temporal trends across sociospatial units, with diverse applications in the social sciences, especially urban science. The two dominant methods are as follows: Time Series Clustering (TSC), with dynamic cluster centres but static labels for each entity, and Sequence Label Analysis (SLA), with static cluster centres but dynamic labels. To implement the universe of models spanning the design space between TSC and SLA, we present tscluster, an open-source Python framework. tscluster offers: (1) several innovative techniques, such as Bounded Dynamic Clustering (BDC), that are not available in existing libraries, allowing users to set an upper bound on the number of label changes and identify the most dynamically evolving time series; (2) a user-friendly interface for applying and comparing these methods; (3) globally optimal solutions for the clustering objective by employing a mixed-integer linear programming formulation, enhancing the reproducibility and robustness of the results in contrast to existing methods based on initialization-sensitive local optimization; and (4) a suite of visualization tools for interpretability and comparison of clustering results. We present our framework using a case study of neighbourhood change in Toronto, comparing two methods available in tscluster. Supplemental materials provide an additional case study of local business development in Chicago and a detailed mathematical exposition of our framework. tscluster can be installed via PyPI (pypi.org/project/tscluster), and the source code is accessible on Github (github.com/tscluster-project/tscluster). Documentation is available online at the tscluster website (tscluster.readthedocs.io).

Stellar Escape from Globular Clusters. II. Clusters May Eat Their Own Tails

We apply for the first time orbit-averaged Monte Carlo star cluster simulations to study tidal tail and stellar stream formation from globular clusters (GCs), assuming a circular orbit in a time-independent spherical Galactic potential. Treating energetically unbound bodies—potential escapers (PEs)—as collisionless enables this fast but spherically symmetric method to capture asymmetric extratidal phenomena with exquisite detail. Reproducing stream features such as epicyclic overdensities, we show how returning tidal tails can form after the stream fully circumnavigates the Galaxy, enhancing the stream's velocity dispersion by several kilometers per second in our ideal case. While a truly clumpy, asymmetric, and evolving Galactic potential would greatly diffuse such tails, they warrant scrutiny as potentially excellent constraints on the Galaxy’s history and substructure. Reexamining the escape timescale Δt of PEs, we find new behavior related to chaotic scattering in the three-body problem; the Δt distribution features sharp plateaus corresponding to distinct locally smooth patches of the chaotic saddle separating the phase-space basins of escape. We study for the first time Δt in an evolving cluster, finding that Δt∼(EJ−0.1,EJ−0.4) for PEs with (low, high) Jacobi energy E J, flatter than for a static cluster ( EJ−2 ). Accounting for cluster mass loss and internal evolution lowers the median Δt from ∼10 Gyr to ≲100 Myr. We finally outline potential improvements to escape in the Monte Carlo method intended to enable the first large grids of tidal tail/stellar stream models from full GC simulations and detailed comparison to stream observations.

Exploiting inherent elasticity of serverless in algorithms with unbalanced and irregular workloads

Function-as-a-Service execution model in serverless computing has been successful in running large-scale computations like MapReduce, linear algebra, and machine learning. However, little attention has been given to executing highly-dynamic parallel applications with unbalanced and irregular workloads. These algorithms are difficult to execute with good parallel efficiency due to the challenge of provisioning the required computing resources in time, leading to resource over- and under-provisioning in clusters of static size. We propose that the elasticity and fine-grained “pay-as-you-go model” of the FaaS model can be a key enabler for effectively running these algorithms in the cloud. We use a simple serverless executor pool abstraction, and evaluate it using three algorithms with unbalanced and irregular workloads. Results show that their serverless implementation can outperform a static Spark cluster of large virtual machines by up to 55% with the same cost, and can even outperform a single large virtual machine running locally.

DBHC: Discrete Bayesian HMM Clustering

Sequence data mining has become an increasingly popular research topic as the availability of data has grown rapidly over the past decades. Sequence clustering is a type of method within this field that is in high demand in the industry, but the sequence clustering problem is non-trivial and, as opposed to static cluster analysis, interpreting clusters of sequences is often difficult. Using Hidden Markov Models (HMMs), we propose the Discrete Bayesian HMM Clustering (DBHC) algorithm, an approach to clustering discrete sequences by extending a proven method for continuous sequences. The proposed algorithm is completely self-contained as it incorporates both the search for the number of clusters and the search for the number of hidden states in each cluster model in the parameter inference. We provide a working example and a simulation study to explain and showcase the capabilities of the DBHC algorithm. A case study illustrates how the hidden states in a mixture of HMMs can aid the interpretation task of a sequence cluster analysis. We conclude that the algorithm works well as it provides well-interpretable clusters for the considered application.

Patterning of the cell cortex by Rho GTPases.

The Rho GTPases - RHOA, RAC1 and CDC42 - are small GTP binding proteins that regulate basic biological processes such as cell locomotion, cell division and morphogenesis by promoting cytoskeleton-based changes in the cell cortex. This regulation results from active (GTP-bound) Rho GTPases stimulating target proteins that, in turn, promote actin assembly and myosin 2-based contraction to organize the cortex. This basic regulatory scheme, well supported by in vitro studies, led to the natural assumption that Rho GTPases function in vivo in an essentially linear matter, with a given process being initiated by GTPase activation and terminated by GTPase inactivation. However, a growing body of evidence based on live cell imaging, modelling and experimental manipulation indicates that Rho GTPase activation and inactivation are often tightly coupled in space and time via signalling circuits and networks based on positive and negative feedback. In this Review, we present and discuss this evidence, and we address one of the fundamental consequences of coupled activation and inactivation: the ability of the Rho GTPases to self-organize, that is, direct their own transition from states of low order to states of high order. We discuss how Rho GTPase self-organization results in the formation of diverse spatiotemporal cortical patterns such as static clusters, oscillatory pulses, travelling wave trains and ring-like waves. Finally, we discuss the advantages of Rho GTPase self-organization and pattern formation for cell function.

Knowledge-Intensive Market: Geomarketing and Cluster Approach to Functioning and Analysis

Relevance. Innovative development is one of the highest priority tasks of the economy at the national and regional level. One of the directions for solving these problems is the formation of local knowledge-intensive markets, within which the interaction of the main participants in the innovation cycle takes place - from conducting fundamental scientific research to introducing a knowledge-intensive product to the market. The object of the study was the local knowledge-intensive market of the Belgorod region. The sources of information for the study were data from the World Intellectual Property Organization (WIPO); research results presented in the eLibrary.Ru search engine; data from statistical reporting of the Territorial Body of the Federal State Statistics Service for the Belgorod Region.The purpose of the study is a cluster grouping of developed advanced production technologies in organizations of the Belgorod region.Objectives: content analysis of theoretical data on the research problem in the eLibrary.Ru search engine; analysis of thematic publications and regulatory documents, data from foreign countries; classification of tools for analyzing indicators of knowledge-intensive markets; grouping of quantitative indicators; testing of research results in the conditions of a regional knowledge-intensive market.Methodology. Retrospective content analysis, static data analysis, cluster analysis and grouping of developed advanced production technologies in organizations of the Belgorod region were used as research methods.Results. The article presents a brief analysis of the effectiveness of implementing these tasks from the perspective of geomarketing and cluster approaches. The main stages of the formation of cluster policy in the Russian Federation are highlighted and the conditions for the formation of this policy at the national level are analyzed. As a result of the research, the main geomarketing tools for analyzing the local knowledge-intensive market were structured; a grouping of quantitative parameters is proposed for conducting geomarketing and cluster analysis of a knowledge-intensive local market.Conclusion. Based on cluster analysis, advanced production technologies used in the region were grouped into four clusters, which can become the basis for further innovative development.

A hybrid framework for robust dynamic 3D point clouds removal

When robot creates a map, dynamic objects can change the space and render the map unusable for navigation. Additionally, the vertical resolution of a VLP-16 LiDAR may be insufficient, making dynamic point removal challenging. To address these challenges, we propose a novel method for dynamic point detection and removal consisting of four components. Firstly, we introduce a multi-resolution heightmap to enhance the efficiency and precision of dynamic point recognition by segmenting ground points. Secondly, we address the issue of limited vertical resolution by fusing multiple scans to simulate additional scan lines and leveraging a multi-resolution range image for precise dynamic point elimination. Thirdly, we apply clustering and principal component analysis-based techniques to compute eigenvectors, facilitating the correction of misclassified static points. Lastly, we propose the utilization of a three-dimensional bounding box strategy to reinforce the monitoring of small static clusters with elevated probabilities of misclassification. These four components complement each other and are executed sequentially. We evaluated our method for both dynamic point removal and ground segmentation on the KITTI dataset and real-world environments. The results demonstrate that our method outperforms baseline methods and generates clean maps.

A 3D Non-Stationarity and Consistency Model for Cooperative Multi-Vehicle Channels

In this paper, based on the irregular shaped geometry-based stochastic modeling (IS-GBSM) approach, a novel three-dimensional (3D) model for cooperative multi-vehicle communication channels is developed. By employing the cooperative communication technology, multi-vehicles can form a platoon to cooperatively transmit data streams, which can achieve distributed multiple-input multiple-output (MIMO) system performance. The proposed cooperative IS-GBSM distinguishes static and dynamic clusters, where continuously arbitrary vehicular movement trajectories (VMTs) of dynamic clusters and multi-vehicles are imitated. A new method, which combines frequency-dependent path gain and spectral clustering algorithm in machine learning (ML), is proposed to model cooperative space-time-frequency (S-T-F) non-stationarity with cooperative time-space (T-S) consistency. Based upon Poisson and Bernoulli processes, the developed method can also model the disappearance of links/sub-channels due to the destruction of transceivers by missiles in an optional manner. Therefore, the proposed cooperative IS-GBSM can further mimic an actual military cooperative communication scenario, i.e., cooperative multiple-unmanned ground vehicle (multi-UGV) communications for Mosaic Warfare. From the proposed cooperative IS-GBSM, typical channel statistics are derived. Simulation results show that cooperative S-T-F non-stationarity with cooperative T-S consistency is mimicked and the influences of vehicular traffic density (VTD) and VMT on channel statistics are investigated. Finally, simulation results fit well with the ray-tracing-based results, confirming the accuracy of the proposed cooperative IS-GBSM.

Worldwide transmission of ST11-KL64 carbapenem-resistant Klebsiella pneumoniae: an analysis of publicly available genomes.

ST11-KL64 is an internationally distributed lineage of carbapenem-resistant Klebsiella pneumoniae and is the most common type in China. The international and interprovincial (in China) transmission of ST11-KL64 CRKP remains to be elucidated. We used both static clusters defined based on a fixed cutoff of ≤21 pairwise single-nucleotide polymorphisms and dynamic groups defined by modeling the likelihood to be linked by a transmission threshold to investigate the transmission of ST11-KL64 strains based on genome sequences mining. We analyzed all publicly available genomes (n = 730) of ST11-KL64 strains, almost all of which had known carbapenemase genes with KPC-2 being dominant. We identified 4 clusters of international transmission and 14 clusters of interprovincial transmission across China of ST11-KL64 strains. We found that dynamic grouping could provide further resolution for determining clonal relatedness in addition to the widely adopted static clustering and therefore increases the confidence for inferring transmission.IMPORTANCECarbapenem-resistant Klebsiella pneumoniae (CRKP) is a serious challenge for clinical management and is prone to spread in and between healthcare settings. ST11-KL64 is the dominant CRKP type in China with a worldwide distribution. Here, we used two different methods, the widely used clustering based on a fixed single-nucleotide polymorphism (SNP) cutoff and the recently developed grouping by modeling transmission likelihood, to mine all 730 publicly available ST11-KL64 genomes. We identified international transmission of several strains and interprovincial transmission in China of a few, which warrants further investigations to uncover the mechanisms for their spread. We found that static clustering based on ≤21 fixed SNPs is sensitive to detect transmission and dynamic grouping has higher resolutions to provide complementary information. We suggest the use of the two methods in combination for analyzing transmission of bacterial strains. Our findings highlight the need of coordinated actions at both international and interprovincial levels for tackling multi-drug resistant organisms.

EPR parameters and spectrum of the solid-state L-α-alanine: cluster or periodic, static or dynamic

L-α-alanine (LαA) is a non-essential α-amino acid in the human body. Free radicals formations in LαA induced by solid-state radiation have been the subject of investigations by electron paramagnetic resonance (EPR) spectroscopy. The Density Functional Theory (DFT) formalism has been used to calculate the EPR parameters of the radiation-induced radicals in the solid-state LαA in this article. Here, the effects of various factors such as the kind of hydrogen bonding (short and long-range) and the temperature have considered to calculate the EPR parameters. An atomic static cluster model has been used to evaluate the short-range hydrogen bonding effects, and Molecular Dynamic (MD) cluster model has been used simultaneously to study the influence of the short-range hydrogen bonding and temperature. A static periodic technique has been used to consider hydrogen bonds (short and long-range) effect. Finally, the MD periodic calculations have been used simultaneously to study the influence of the hydrogen bonds and temperature. These MD periodic calculations are more consistent with experiments than static periodic calculations and clusters. The EPR spectrums of LαA powder and crystal have been simulated and compared with the experimental EPR spectrums. The results show that they are in good agreement with the experimental results.

A Non-Stationary 6G V2V Channel Model With Continuously Arbitrary Trajectory

In this article, a novel three-dimensional (3D) massive multiple-input multiple-output (MIMO) millimeter wave (mmWave) geometry-based stochastic model (GBSM) is proposed for sixth-generation (6G) vehicle-to-vehicle (V2V) channels. In the proposed GBSM, clusters in the environment are divided into static clusters and dynamic clusters. Furthermore, the time-variant acceleration together with the integration of time during the transmission distance update are exploited. As a result, the continuously arbitrary trajectory of the transceiver and dynamic clusters is successfully captured. To jointly model space-time-frequency (S-T-F) non-stationarity of 6G V2V channels, a new method, which properly integrates the frequency-dependent factor, birth-death (BD) process, and selective evolution of static and dynamic clusters, is developed. Key channel statistics, including the space-time-frequency correlation function (STF-CF), time stationary interval, and Doppler power spectral density (DPSD) are obtained. Simulation results demonstrate that S-T-F non-stationarity is modeled and the impacts of vehicular traffic density (VTD) and vehicular movement trajectory (VMT) on channel statistics are further analyzed thoroughly. Finally, the generality and accuracy of the proposed GBSM are validated through the comparison of simulation results and available measurement data.

An Energy-Efficient Protocol for Internet of Things Based Wireless Sensor Networks

The performance of Wireless Sensor Networks (WSNs) is an important fragment of the Internet of Things (IoT), where the current WSNbuilt IoT network’s sensor hubs are enticing due to their critical resources. By grouping hubs, a clustering convention offers a useful solution for ensuring energy-saving of hubs and Hybrid Media Access Control (HMAC) during the course of the organization. Nevertheless, current grouping standards suffer from issues with the grouping structure that impacts the exhibition of these conventions negatively. In this investigation, we recommend an Improved Energy-Proficient Algorithm (IEPA) for HMAC throughout the lifetime of the WSN-based IoT. Three consecutive segments are suggested. For the covering of adjusted clusters, an ideal number of clusters is determined first. Then, fair static clusters are shaped, based on an updated calculation for fluffy cluster heads, to reduce and adapt the energy use of the sensor hubs. Cluster heads (CHs) are, ultimately, selected in optimal locations, with the pivot of the cluster heads working among cluster members. Specifically, the proposed convention diminishes and balances the energy utilization of hubs by improving the grouping structure, where the IEPA is reasonable for systems that need a long time. The assessment results demonstrate that the IEPA performs better than existing conventions.

Assessing Through-Water Structure-from-Motion Photogrammetry in Gravel-Bed Rivers under Controlled Conditions

Structure-from-Motion (SfM) photogrammetry has become a popular solution for three-dimensional topographic data collection in geosciences and can be used for measuring submerged bed surfaces in shallow and clear water systems. However, the performance of through-water SfM photogrammetry has not been fully evaluated for gravel-bed surfaces, which limits its application to the morphodynamics of gravel-bed rivers in both field investigations and flume experiments. In order to evaluate the influence of bed texture, flow rate, ground control point (GCP) layout, and refraction correction (RC) on the measurement quality of through-water SfM photogrammetry, we conducted a series of experiments in a 70 m-long and 7 m-wide flume with a straight artificial channel. Bed surfaces with strongly contrasting textures in two 4 m-long reaches were measured under five constant flow regimes with three GCP layouts, including both dry and underwater GCPs. All the submerged surface models with/without RC were compared with the corresponding dry bed surfaces to quantify their elevation errors. The results illustrated that the poorly sorted gravel-bed led to the better performance of through-water SfM photogrammetry than the bed covered by fine sand. Fine sediment transport caused significant elevation errors, while the static sand dunes and grain clusters did not lead to noticeable errors in the corrected models with dry GCPs. The elevation errors of the submerged models linearly increased with water depth for all the tested conditions of bed textures, GCP layouts, and discharges in the uncorrected models, but the slopes of the increasing relations varied with texture. The use of underwater GCPs made significant improvements to the performance of direct through-water SfM photogrammetry, but counteracted with RC. The corrected models with dry GCPs outperformed the uncorrected ones with underwater GCPs, which could still be used to correct the underestimation in surface elevation caused by RC. Based on the new findings, recommendations for through-water SfM photogrammetry in measuring submerged gravel-bed surfaces were provided.

An Energy-Efficient Clustering Method for Target Tracking Based on Tracking Anchors in Wireless Sensor Networks.

As a key technology in wireless sensor networks (WSNs), target tracking plays an essential role in many applications. To improve energy efficiency, clustering is widely used in tracking to organize the network to achieve data fusion and reduce communication costs. Many existing studies make dynamic adjustments based on static clusters to track moving targets. However, the additional overhead caused by frequent cluster reconstruction and redundant data transmission is rarely considered. To address this issue, we propose a tracking-anchor-based clustering method (TACM) in this paper, in which tracking anchors are introduced to provide activation indications for sensors according to the target position. We use the rough fuzzy C-means (RFCM) algorithm to locate the anchors and use the membership table to activate sensors to form a cluster. Since there are no sending, receiving, and fusing data tasks for anchors, they are lightly burdened and can significantly reduce the frequency of being rotated. Moreover, the state of cluster members (CMs) is scheduled using the linear 0–1 programming to reduce redundant transmissions. The simulation results demonstrate that, compared with some existing clustering methods, the proposed TACM effectively reduces the energy consumption when tracking a moving target, thus prolonging the network lifetime.

Performance analysis of Heterogeneous Cloud-Radio Access Networks: A user-centric approach with network scalability

Heterogeneous cloud-radio access network (HC-RAN) is a very promising network architecture for future generation wireless communication systems. The centralized computation of HC-RAN provides flexibility to coordinate multi-point (CoMP) transmission of remote radio heads (RRHs). These RRHs can cooperatively form static or dynamic clusters which are network-centric or user-centric to serve the user equipment (UE). To meet the demands of future generation wireless networks, the user-centric approach is gaining much attention, especially in dynamic clustering. In dynamic user-centric CoMP, a set of overlapped clusters can cooperate dynamically to serve UEs. In a large network, the main challenge is the scalability of the computational complexity and fronthaul load with the increase of the number of UEs. To address this, we developed a scalable user-centric HC-RAN by utilizing dynamic cooperation clustering (DCC) framework. We presented an algorithm for joint user association, resource allocation, and cluster formation. We derived the expressions for different combining and precoding vectors to satisfy the scalability condition. We evaluated the performance of the scalable user-centric HC-RAN in terms of the achievable rate per UE at two different levels of cooperation with imperfect channel state information (CSI). The performance of the derived schemes is nearly optimal compared to the unscalable benchmark schemes and the ideal scalable system.

A 3-D Non-Stationary Model for Beyond 5G and 6G Vehicle-to-Vehicle mmWave Massive MIMO Channels

This paper proposes a novel three-dimensional (3D) non-stationary irregular-shaped geometry-based stochastic model (IS-GBSM) for beyond fifth-generation (B5G) and sixth-generation (6G) vehicle-to-vehicle (V2V) millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) channels. By distinguishing dynamic clusters and static clusters, the proposed IS-GBSM for the first time explores the impact of vehicular traffic density (VTD) on channel statistics in B5G/6G V2V mmWave massive MIMO scenarios. Furthermore, a novel method is developed to model the channel space-time-frequency non-stationarity. In the developed method, dynamic/static correlated clusters are first generated by an improved <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> -Means clustering algorithm. Then, by employing a birth-death process based on correlated clusters, the consistency in birth and death between dynamic/static correlated clusters during time-array evolution is modeled. Key channel statistical properties, e.g., space-time-frequency correlation function (STF-CF) and Doppler power spectral density (DPSD), are derived. Simulation results demonstrate that the space-time-frequency non-stationarity is captured and the influence of VTDs on channel statistics is explored. Finally, the close agreement is achieved between simulation results and measurements, verifying the utility of proposed IS-GBSM.

Machine Learning-Based Multipath Components Clustering and Cluster Characteristics Analysis in High-Speed Railway Scenarios

Channel multipath components (MPCs) clustering and cluster characterization are the prerequisite for the development of cluster based channel models. This article investigates the MPCs clustering based on machine learning (ML) and analyzes the cluster characteristics in typical high-speed railway (HSR) scenarios. A variational Bayesian Gaussian mixture model (GMM)-based algorithm is introduced to achieve the space–time clustering of MPCs, which incorporates the statistical characteristics of MPCs and can automatically determine the optimum number of clusters. Moreover, a novel density-based validity index is proposed for evaluating the MPCs clustering performance. The proposed validity index improves the traditional index by considering the intracluster density, which can be calculated according to the Graham scanning method and Green’s formula. In addition to synthetic datasets, realistic MPCs datasets collected in an HSR obstructed viaduct scenario are used for the performance evaluation of the clustering algorithm and clustering validity index. Based on clustering results in the measured scenario, static cluster characteristics, including cluster number, intercluster and intracluster delay spreads (DSs) and angle spreads (ASs), and dynamic cluster characteristics such as cluster lifetime and birth and death property of clusters, are extracted and analyzed. These results will be useful for cluster-based channel modeling in future HSR mobile communication systems.

Swarmalators under competitive time-varying phase interactions

Swarmalators are entities with the simultaneous presence of swarming and synchronization that reveal emergent collective behavior due to the fascinating bidirectional interplay between phase and spatial dynamics. Although different coupling topologies have already been considered, here we introduce time-varying competitive phase interaction among swarmalators where the underlying connectivity for attractive and repulsive coupling varies depending on the vision (sensing) radius. Apart from investigating some fundamental properties like conservation of center of position and collision avoidance, we also scrutinize the cases of extreme limits of vision radius. The concurrence of attractive–repulsive competitive phase coupling allows the exploration of diverse asymptotic states, like static π, and mixed phase wave states, and we explore the feasible routes of those states through a detailed numerical analysis. In sole presence of attractive local coupling, we reveal the occurrence of static cluster synchronization where the number of clusters depends crucially on the initial distribution of positions and phases of each swarmalator. In addition, we analytically calculate the sufficient condition for the emergence of the static synchronization state. We further report the appearance of the static ring phase wave state and evaluate its radius theoretically. Finally, we validate our findings using Stuart–Landau oscillators to describe the phase dynamics of swarmalators subject to attractive local coupling.

Sonar-Based Human Leg Localization Using Chaos Enhanced Dynamic Neighborhood Learning-Based GSA Aided sNDT Algorithm

The present paper proposes a new human leg localization algorithm using ultrasonic sensors in human-robot coexisting environments. The algorithm estimates the motion of a human leg pair between two successive sonar scans by using a new <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">static cluster elimination</i> method, an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">edge feature based leg recognition</i> algorithm and an advanced scan matching technique. We also propose a novel, robust approach to overcome <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">bad initialization</i> problem in sonar scan matching, by introducing a metaheuristic search based optimization algorithm for the sonar NDT (sNDT) method. The recently proposed dynamic neighborhood learning-based GSA (DNLGSA) has been successfully utilized in real-life scenario to solve this problem. The work also proposes a new chaos enhanced DNLGSA (CEDNLGSA) to further improve real-life performance and the proposed novel variant of the sNDT method based on CEDNLGSA, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Chaotic Metaheuristic Search Based sNDT</i> (CMHS-sNDT), has been demonstrated to achieve superior leg detection performance in various real-life case studies, compared to different contemporary state-of-the-art methods.

A 3-D Nonstationary Wideband V2V GBSM With UPAs for Massive MIMO Wireless Communication Systems

This article proposes a novel 3-D nonstationary wideband vehicle-to-vehicle (V2V) geometry-based stochastic model (GBSM) with uniform planar antenna arrays (UPAs) for massive multiple-input–multiple-output (MIMO) wireless communication systems. In the proposed GBSM, a novel method, so-called birth–death (BD) process and seed algorithm-based selective cluster evolution, is developed to capture the space nonstationarity of V2V massive MIMO with UPA channels. The time nonstationarity is further mimicked by employing this novel method over the entire timeline. In addition, the proposed GBSM not only models the reflection of the ground, but also divides clusters into static clusters and dynamic clusters to sufficiently investigate the impact of vehicular traffic density (VTD) on channel statics. The channel parameters are properly calculated by 3-D vectors, resulting in the proposed GBSM with high accuracy and low complexity. Important statistical properties, such as the space-time correlation function (S-T CF), spatial cross-correlation function (CCF), temporal auto-correlation function (ACF), and Doppler power spectrum density (PSD) are derived and thoroughly investigated. Simulation results show that the space-time nonstationarity is successfully mimicked and the VTD has a significant impact on channel statistics. Finally, an excellent agreement is achieved between simulation results and measurements, validating the accuracy of the proposed GBSM.