Analysis of multipath propagation structure in urban mobile radio environments

Ikegami, Fumio; Yoshida, S.

doi:10.1109/tap.1980.1142372

Cited by 81 publications

(37 citation statements)

References 2 publications

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“…As highlighted in [1], the key aspect when dealing with learning machines is the choice of the inputs. In macrocellular models [16,24], propagation loss is calculated as the sum of the free space path loss (L 0 ) and an attenuation term (α buildings ) which takes into account the effect of the buildings:…”

Section: A New Model For Path Loss Predictionmentioning

confidence: 99%

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Path loss prediction in urban environment using learning machines and dimensionality reduction techniques

Piacentini

Rinaldi

2010

Comput Manag Sci

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Path loss prediction is a crucial task for the planning of networks in modern mobile communication systems. Learning machine-based models seem to be a valid alternative to empirical and deterministic methods for predicting the propagation path loss. As learning machine performance depends on the number of input features, a good way to get a more reliable model can be to use techniques for reducing the dimensionality of the data. In this paper we propose a new approach combining learning machines and dimensionality reduction techniques. We report results on a real dataset showing the efficiency of the learning machine-based methodology and the usefulness of dimensionality reduction techniques in improving the prediction accuracy.

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Section: A New Model For Path Loss Predictionmentioning

confidence: 99%

“…Results are usually obtained by means of measurement campaigns. In deterministic models [16,24] the field strength is calculated using the Geometrical Theory of Diffraction (GTD). It is obtained as the superposition of direct, reflected and diffracted rays at the point of interest.…”

Section: Introductionmentioning

confidence: 99%

Path loss prediction in urban environment using learning machines and dimensionality reduction techniques

Piacentini

Rinaldi

2010

Comput Manag Sci

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“…The principal mechanism for making the source appear to be distributed in space is diffuse (unresolvable) and specular (resolvable) multipath caused by scattering of the propagating waves. For instance, experimental results obtained in urban wireless communications reported significant angular scattering distributions due to local scattering and reflection from mobile stations [2], [10], [12] and base stations [7], [17]. The characterization of the power azimuth spectrum shows that angular spreads as large as 25 have been observed.…”

Section: Introductionmentioning

confidence: 99%

Detection of Distributed Sources Using Sensor Arrays

Jin

Friedlander

2004

IEEE Trans. Signal Process.

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Abstract-In this paper, we consider the problem of detecting a random spatially distributed signal source by an array of sensors. We start with an approximate likelihood ratio (LR) detector and analyze its performance. Using the generalized likelihood ratio (GLR) approach, we then derive detectors under several assumptions on the available statistics. The performance of these detectors is evaluated, and the effect of the angular spread of the source is investigated. The detection performance behaves differently under different scenarios. We notice that the degrees of freedom (DOF) of the distributions of the detection statistics depend on both the signal angular spread and the number of data snapshots. Specifically, at a high SNR level and with small degrees of freedom, an increase of angular spread improves the detection performance. However, with large degrees of freedom, the increase of angular spread reduces detection performance. We provide a detailed discussion of the behavior of detection performance under various conditions. A comparison between the GLR detectors and conventional beamformer detectors is made by computer simulations. The results indicate that the GLR detectors perform better as the angular spread becomes large than that of the conventional beamformer detectors.Index Terms-Detection, distributed source, generalized likelihood ratio (GLR), sensor array.

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“…Result of this development is a growing need for a more fundamental understanding of the dominant propagation mechanisms in the mobile radio channel. For this purpose, a number of groups have been active in the measurement of channel characteristics at the level of the individual multipath components, in particular, by separating the multipath waves on the basis of their different propagation delay times and angles-of-arrival (AoA's) [1]- [6]. Delay measurements are usually performed in the time domain using the pseudonoise (PN) correlation approach [7], [8].…”

Section: Introductionmentioning

confidence: 99%

“…Delay measurements are usually performed in the time domain using the pseudonoise (PN) correlation approach [7], [8]. For the measurement of AoA's, both directional antennas [1] and (synthetic) phased arrays have been applied, the latter of which benefit from their relative ease of use and suitability for digital data processing schemes such as high-resolution algorithms. Synthetic arrays have the inherent advantages that mutual coupling between adjacent array elements is avoided, and that the radiation pattern of the elements is not affected.…”

Section: Introductionmentioning

confidence: 99%