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Three kinds of layout in cellular networks. (a) Parallelogram layout, (b) Right triangle layout, (c) Star layout. • Selected station, � Unselected station. https://doi.org/10.1371/journal.pone.0272487.g005
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For the problem of passive location in mobile cellular network, base stations (BSs) selection can improve positioning accuracy. Through the analysis of base station layout in cellular networks, using Geometric Dilution of Precision (GDOP) as the optimization objective, we propose a Dynamic Base Stations Selection (DBSS) method in a cellular unit. T...
Contexts in source publication
Context 1
... base stations can form a variety of distribution layout [19]. However, in the architecture of cellular cell, there are only three station layouts to choose, which are shown in Fig 5. One is a parallelogram layout shown in Fig 5(a), selecting F, A, B and O stations. Another is a right triangle layout shown in Fig 5(b), selecting A, C, O and F stations. ...
Context 2
... in the architecture of cellular cell, there are only three station layouts to choose, which are shown in Fig 5. One is a parallelogram layout shown in Fig 5(a), selecting F, A, B and O stations. Another is a right triangle layout shown in Fig 5(b), selecting A, C, O and F stations. ...
Context 3
... is a parallelogram layout shown in Fig 5(a), selecting F, A, B and O stations. Another is a right triangle layout shown in Fig 5(b), selecting A, C, O and F stations. The third one is a star layout shown in Fig 5(c), selecting F, B, D and O stations. ...
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... r B represents the distance between the historical optimal value and the current value. r A represents the distance between the optimal individual value and the current value [31]. (19) where x ij is the j th component of the mayfly i. X i represents the corresponding B ij and A ij . ...
The various applications of bearing-only sensor networks for detection and localization are becoming increasingly widespread and important. The array layout of the bearing-only sensor network seriously impacts the detection performance. This paper proposes a multi-strategy fusion improved adaptive mayfly algorithm (MIAMA) in a bearing-only sensor network to perform layout planning on the geometric configuration of the optimal detection. Firstly, the system model of a bearing-only sensor network was constructed, and the observability of the system was analyzed based on the Cramer–Rao Lower Bound and Fisher Information Matrix. Then, in view of the limitations of the traditional mayfly algorithm, which has a single initial population and no adaptability and poor global search capabilities, multi-strategy fusion improvements were carried out by introducing Tent chaos mapping, the adaptive inertia weight factor, and Random Opposition-based Learning. Finally, three simulation experiments were conducted. Through comparison with the Particle Swarm Optimization (PSO) algorithm, Mayfly Algorithm (MA), and Genetic Algorithm (GA), the effectiveness and superiority of the proposed MIAMA were validated.
... Currently, most research sets an ideal condition for unmanned swarm detection, which neglects the positioning error in many aspects [26,27]. First, before determining the configuration for precise detection of a single target, the target location needs to be provided by a priori information, which should be imprecise in practice. ...
When unmanned platforms perform precise target detection, the configuration of detection nodes will significantly impact accuracy. Aiming to obtain the minimum dilution of precision (DOP), this paper innovatively proposes an optimal detection configuration design method focused on the heterogeneous unmanned cooperative swarm based on the nested cone model. The proposed method first divides the swarm into different groups according to the performances of platforms and then uses a conical nested configuration to arrange the placement of each node independently. The paper considers the problem of the inaccurate prior position of the target and replaces the single-point DOP with the average DOP on the prior region of the target as the optimization objective. Considering the unavoidable positioning errors in engineering practice, this paper provides the optimal configuration of the detection group (DG) and anchor group (AG) in the swarm to reduce the impact caused by positioning errors of detection nodes. We set a certain swarm consisting of 3 types of platforms to design the configuration by simulation experiments and find the optimal parameters for nested cones to realize accurate detection.
... The Dynamic Base Station Selection (DBSS) method, proposed in [57], enables cellular systems to dynamically select the positioning base station for the UE location estimation, specifically for the case of four and five base stations. The drawback of the proposed approach is the assumption about regular hexagonal base station arrangements on the plane. ...
... The choice of four gNBs for the topology search can be explained by the fact that existing work in this field [50][51][52][53][54][55][56][57][58][59][60][61] usually treats the layout of four base stations. To compare the presented results with known ones, we used the PDOP criterion with TOA and TDOA primary measurement processing. ...
... Such an approach will make it possible to search for and justify gNB topology in UDN scenarios. The choice of four gNBs for the topology search can be explained by the fact that existing work in this field [50][51][52][53][54][55][56][57][58][59][60][61] usually treats the layout of four base stations. To compare the presented results with known ones, we used the PDOP criterion with TOA and TDOA primary measurement processing. ...
User equipment (UE) location estimation in emerging 5G/B5G/6G Ultra-Dense Networks (UDNs) is a breakthrough technology in future wireless info-communication ecosystems. Apart from communication aspects, network infrastructure densification promises significant improvement in UE positioning accuracy. Unlike networks of previous generations, an increased number of gNodeBs (gNBs) per unit area and/or volume in UDNs allows to perform measurements for UE positioning only with those base stations whose topologies are most suitable from the geometric point of view. Quantitative measurements of gNB topology suitability include horizontal (HDOP), vertical (VDOP), and position (PDOP) dilution of the precision (DOP) criteria on the plane, in height, and in space, respectively. In the current work, we formalize a set of methods for gNB topology search using time of arrival (TOA), time difference of arrival (TDOA), angle of arrival (AOA), and combined TOA–AOA and TDOA-AOA measurements. The background of the topology search using DOP criteria is a significantly increased number of gNBs per unit volume in UDNs. Based on a simulation, we propose a novel approach for a topology search in a positioning service area, resulting in a PDOP less than one for the Gazprom Arena with only five gNBs. The contribution of the current research includes algorithm and software for an iterative search of all possible gNB and UE locations in space, minimizing UE geometric DOP. The practical application of the algorithm is the gNB topology substantiation for the given positioning scenarios in 5G/B5G/6G UDNs.
