Discrete Method of Images for 3D Radio Propagation Modeling

Novak, Roman

doi:10.1007/s13319-016-0102-y

Cited by 3 publications

(2 citation statements)

References 25 publications

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“…Alternatively, the analytical and simulation-based approaches (e.g. radio ray-traycing [6]) can supplement measurements to some extent. The simulated CSI accuracy, however, depends on the span of considered phenomena (see [2, p. 22]) and knowledge about a particular radio environment.…”

Section: Imentioning

confidence: 99%

Improving CSI-based Massive MIMO Indoor Positioning using Convolutional Neural Network

Cerar,

Švigelj,

Mohorčič

et al. 2021

Preprint

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Multiple-input multiple-output (MIMO) is an enabling technology to meet the growing demand for faster and more reliable communications in wireless networks with a large number of terminals, but it can also be applied for position estimation of a terminal exploiting multipath propagation from multiple antennas. In this paper, we investigate new convolutional neural network (CNN) structures for exploiting MIMO-based channel state information (CSI) to improve indoor positioning. We evaluate and compare the performance of three variants of the proposed CNN structure to five NN structures proposed in the scientific literature using the same sets of trainingevaluation data. The results demonstrate that the proposed residual convolutional NN structure improves the accuracy of position estimation and keeps the total number of weights lower than the published NN structures. The proposed CNN structure yields from 2 cm to 10 cm better position accuracy than known NN structures used as a reference.

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Section: Imentioning

confidence: 99%

Improving CSI-based Massive MIMO Indoor Positioning using Convolutional Neural Network

Cerar,

Švigelj,

Mohorčič

et al. 2021

Preprint

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“…For instance, radio signal ray tracing is not much different from the established methods in computer graphics, which heavily rely on the hardware acceleration. In previous work, the author showed that even standard rendering can be used to accelerate radiofrequency channel modeling [78]. For example, Fig.…”

Section: Use Of Hardware Acceleratorsmentioning

confidence: 99%

Viability of Numerical Full-Wave Techniques in Telecommunication Channel Modelling

Novak

2020

J. commun. softw. syst. (Online)

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In telecommunication channel modelling the wavelength is small compared to the physical features of interest, therefore deterministic ray tracing techniques provide solutions that are more efficient, faster and still within time constraints than current numerical full-wave techniques. Solving fundamental Maxwell's equations is at the core of computational electrodynamics and best suited for modelling electrical field interactions with physical objects where characteristic dimensions of a computing domain is on the order of a few wavelengths in size. However, extreme communication speeds, wireless access points closer to the user and smaller pico and femto cells will require increased accuracy in predicting and planning wireless signals, testing the accuracy limits of the ray tracing methods. The increased computing capabilities and the demand for better characterization of communication channels that span smaller geographical areas make numerical full-wave techniques attractive alternative even for larger problems. The paper surveys ways of overcoming excessive time requirements of numerical full-wave techniques while providing acceptable channel modelling accuracy for the smallest radio cells and possibly wider. We identify several research paths that could lead to improved channel modelling, including numerical algorithm adaptations for large-scale problems, alternative finite-difference approaches, such as meshless methods, and dedicated parallel hardware, possibly as a realization of a dataflow machine.

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Improving CSI-based Massive MIMO Indoor Positioning using Convolutional Neural Network

Cerar

Švigelj²,

Mohorčič³

et al. 2021

2021 Joint European Conference on Networks and Communications &Amp; 6G Summit (EuCNC/6G Summit)

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Discrete Method of Images for 3D Radio Propagation Modeling

Cited by 3 publications

References 25 publications

Improving CSI-based Massive MIMO Indoor Positioning using Convolutional Neural Network

Improving CSI-based Massive MIMO Indoor Positioning using Convolutional Neural Network

Viability of Numerical Full-Wave Techniques in Telecommunication Channel Modelling

Improving CSI-based Massive MIMO Indoor Positioning using Convolutional Neural Network

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