On the statistical modeling of MIMO land mobile satellite channels: a consolidated approach

Liolis, Konstantinos; Gómez-Vilardebó, Jesús; Casini, Enrico; Pérez-Neira, Ana I.

doi:10.1049/cp.2009.1193

Cited by 22 publications

(31 citation statements)

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“…In the case of the LMS, the scatterering rich environment local to the mobile terminal provides low correlation between the antenna branches, while at the satellite there are directional antennas with highly orthogonal circular polarizations, thus their correlation is also low. At each end therefore, the correlation is controlled independently and also remains low in a non line of sight scenario, which therefore justifies that the channel is separable in order to allow the well known Kronecker model to be applied [15] 9) In the case where there is low shadowing and a line of sight (LOS), the channel cannot be considered separable and thus the Kronecker assumption does not hold. Thus the Kronecker model has been extended here such that it is suited to a 2x2 satellite MIMO system in a LOS environment with dual circular polarization, which enables polarization multiplexing.…”

Section: Model Generationmentioning

confidence: 99%

“…However, this model extends the capabilities to the MIMO case, where its stochastic properties are derived from an S-band tree-lined road measurement campaign using dual circular polarizations at low elevations. Furthermore, this model in particular compared to existing models to date [8] [9] considers the interdependence between the small scale fading. The authors have found the most appropriate means to accommodate this interdependence is to use a Ricean fading model, where the co-polar and cross-polar components are suitably correlated.…”

Section: Introductionmentioning

confidence: 99%

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Empirical-Stochastic LMS-MIMO Channel Model Implementation and Validation

King

Brown

Kyrgiazos

et al. 2012

IEEE Trans. Antennas Propagat.

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Abstract-Land Mobile Satellite (LMS) networks, forming a key component of future mobile Internet and broadcasting, can benefit from Multiple-Input Multiple-Output (MIMO) techniques to improve spectral efficiency and outage. LMS-MIMO networks can be obtained using multiple satellites with single polarization antennas with spatial multiplex channel coding, or by a single satellite with dual polarization antennas providing polarization multiplex channel coding. In this paper, a guide is presented showing the steps required to implement a simple empirical-stochastic dual circular polarized LMS-MIMO narrowband channel model with validation both with and without a line of sight. The model is based on an S-band treelined road measurement campaign using dual circular polarizations at low elevations. Application of the model is aimed at LMS-MIMO physical layer researchers and system designers, who need an easy to implement and reliable model, representative of typical LMS-MIMO channel conditions.

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Section: Model Generationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Empirical-Stochastic LMS-MIMO Channel Model Implementation and Validation

King

Brown

Kyrgiazos

et al. 2012

IEEE Trans. Antennas Propagat.

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“…Fig. 13 presents the cumulative distribution functions (CDFs) of the LMS channel capacities for the dual polarized MIMO and SISO cases calculated using (9) and (10), respectively. The channel time series are generated using the simulator shown in Fig.…”

Section: Performance Evaluation a Channel Capacitymentioning

confidence: 99%

“…Availability of realistic channel time series and detailed knowledge of their correlation properties are needed for performing accurate simulations [5]. Several studies on terrestrial MIMO systems can be found in the literature, while studies on LMS MIMO are rather limited, among them are [1], [6]- [9].…”

Section: Introductionmentioning

confidence: 99%

Land Mobile Satellite Dual Polarized MIMO Channel Along Roadside Trees: Modeling and Performance Evaluation

Cheffena

Fontán

Lacoste

et al. 2012

IEEE Trans. Antennas Propagat.

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“…As a starting point, Section 2 summarizes the state-of-the-art concerning dual polarization MIMO in terrestrial wireless and demonstrates the advantages of MIMO for typical channel cases. Section 3 briefly reviews the most comprehensive MIMO LMS channel model available in the literature [12]. The concepts described in these sections are then applied in Section 4 for a detailed MIMO versus SISO comparison in LMS broadcasting systems.…”

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confidence: 99%

Dual polarization MIMO in LMS broadcasting systems: Possible benefits and challenges

Arapoglou

Zamkotsian

Cottis

2010

Satell Commun Network

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SUMMARYThe benefits of employing dual polarization in wireless multiple-input multiple-output (MIMO) communication systems are only recently becoming apparent due to the continuous measurement and modeling efforts by the terrestrial research community. Land mobile satellite (LMS) broadcasting systems exhibit distinct channel, system and geometrical characteristics and are subjected to stringent spatial limitations compared to terrestrial wireless. Therefore, it remains to be investigated which operating conditions favor the joint instead of the independent encoding/decoding over the two polarizations of satellite beams. After outlining the relevant progress made in wireless dual polarization MIMO systems, the present work carries out a thorough comparison between the joint and independent encoding/decoding approaches in typical single-satellite LMS systems exploiting state-of-the-art building blocks. This comparative analysis investigates both the capacity and error performance under a fair framework between systems occupying the same resources. Since the same analytical tools are used for both terrestrial and satellite dual polarization systems, the present paper may also serve as an analytical bridge between the two.

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On the statistical modeling of MIMO land mobile satellite channels: a consolidated approach

Cited by 22 publications

References 0 publications

Empirical-Stochastic LMS-MIMO Channel Model Implementation and Validation

Empirical-Stochastic LMS-MIMO Channel Model Implementation and Validation

Land Mobile Satellite Dual Polarized MIMO Channel Along Roadside Trees: Modeling and Performance Evaluation

Dual polarization MIMO in LMS broadcasting systems: Possible benefits and challenges

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