An advanced field prediction model including diffuse scattering

Degli-Esposti, Vittorio; Guiducci, Doriana; de'Marsi, A.; Azzi, P.; Fuschini, Franco

doi:10.1109/tap.2004.831299

Cited by 151 publications

(86 citation statements)

References 4 publications

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“…Finally, the total electric field is obtained by summing the electric fields of the rays and the electric fields of the scattering waves. Note that the details of the calculation and a comparison to measurement results are given in [37] and [38].…”

Section: Hybrid With Effective Roughness Modelmentioning

confidence: 99%

A Survey of Efficient Ray-Tracing Techniques for Mobile Radio Propagation Analysis

Imai

2017

IEICE Trans. Commun.

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Tetsuro IMAI †a) , Member SUMMARY With the advances in computer processing that have yielded an enormous increase in performance, numerical analytical approaches based on electromagnetic theory have recently been applied to mobile radio propagation analysis. One such approach is the ray-tracing method based on geometrical optics and the uniform geometrical theory of diffraction. In this paper, ray-tracing techniques that have been proposed in order to improve computational accuracy and speed are surveyed. First, imaging and ray-launching methods are described and their extended methods are surveyed as novel fundamental ray-tracing techniques. Next, various ray-tracing acceleration techniques are surveyed and categorized into three approaches, i.e., deterministic, heuristic, and brute force. Then, hybrid methods are surveyed such as those employing Physical optics, the Effective Roughness model, and the Finite-Difference Time-Domain method that have been proposed in order to improve analysis accuracy.

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Section: Hybrid With Effective Roughness Modelmentioning

confidence: 99%

A Survey of Efficient Ray-Tracing Techniques for Mobile Radio Propagation Analysis

Imai

2017

IEICE Trans. Commun.

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“…The Parameters (θ) used for identifying the model s(θ) may be estimated using, iterative Maximum Likelihood (ML) [25], or Extended Kalman Filter (EKF) [26], [27]. Both of these estimation methods rely on the evaluation of the expression…”

Section: The Fim -Key Quantity Of Parameter Estimation 1 Computatmentioning

confidence: 99%

A Survey on PARATREE and SUSVD Decomposition Techniques and Their Use in Array Signal Processing

Bhatt¹,

Kumar²

2013

IJIGSP

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-The present manuscript is intended to review few applications of tensor decomposition model in array signal processing. Tensor decomposition models like HOSVD, SVD and PARAFAC are useful in signal processing. In this paper we shall use higher order tensor decomposition in signal processing. Also, a novel orthogonal non-iterative tensor decomposition technique (SUSVD), which is scalable to arbitrary high dimensional tensor, has been applied in MIMO channel estimation. The SUSVD provides a tensor model with hierarchical tree structure between the factors in different dimensions. We shall use a new model known as PARATREE, which is related to PARAFAC tensor models. The PARAFAC and PARATREE both describe a tensor as a sum of rank-1 tensors, but PARATREE has several advantages over PARAFAC, when it is applied as a lower rank approximation technique. PARATREE is orthogonal, fast and reliable to compute, and the order of the decomposition can be adaptively adjusted. The low rank PARATREE approximation has been applied to measure noise suppression for tensor valued MIMO channel sounding measurements.

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“…100 scatterers in a snapshot costs about 8×10 6 flops which can be executed in less than 0.1s using the computing software, e.g., MATLABő with a quad-core computer. In addition, the IR obtained by the graph approach exhibits the specular-to-diffuse transition, which is hard to obtain using conventional ray-tracing-based channel modeling with tractable complexity [9][10][11].…”

Section: A Brief Introduction Of Graph Modelingmentioning

confidence: 99%

Spatial cross-correlation modeling for propagation channels in indoor distributed antenna systems

Tian

Yin

Zhou

et al. 2013

J Wireless Com Network

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In this contribution, the spatial cross-correlation of composite channels in a distributed antenna system (DAS) is studied for the case that a nine-antenna DAS is deployed in an indoor environment. As few measurement data of DAS propagation channels are available, the propagation graph modeling based on the electromagnetic wave reverberation theory is used to generate the synthetic impulse responses (IRs) for the composite channels induced by the different groups of antennas in the DAS. The simulations take into account the geographic parameters of the environment, the cluttering scatterers, the realistic visibility conditions, as well as the flexible locations for the antennas, and the user equipment. The uncorrelated scattering assumption is proved to be valid by using the real measurement data from the indoor environments. Based on this assumption, narrowband channel cross-correlation is computed by integrating the small-scale fading cross-correlation within the same delay bins in two channel IRs. The characteristics of the cross-correlation in four cases with different antenna grouping are investigated for the nine-antenna DAS. Part of the modeling results are shown to be consistent with the empirical counterparts for specific DAS constellations calculated using the real measurement data.

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An advanced field prediction model including diffuse scattering

Cited by 151 publications

References 4 publications

A Survey of Efficient Ray-Tracing Techniques for Mobile Radio Propagation Analysis

A Survey of Efficient Ray-Tracing Techniques for Mobile Radio Propagation Analysis

A Survey on PARATREE and SUSVD Decomposition Techniques and Their Use in Array Signal Processing

Spatial cross-correlation modeling for propagation channels in indoor distributed antenna systems

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