First-Order Statistics Prediction for a Propagation Channel of Arbitrary Non-Geostationary Satellite Orbits

Kvičera, Milan; Pechač, Pavel

doi:10.13164/re.2015.0093

Cited by 1 publication

(1 citation statement)

References 15 publications

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“…The first‐order statistics include the probability or cumulative distribution functions (pdf or cdf), whereas the second‐order statistics incorporate parameters such as the autocorrelation function, the level crossing rate (LCR), and the average fade duration (AFD). In this context, there are scarce statistical characterization attempts, mainly in the frame of King and Stavrou, King and Brown et al, Kourogiorgas, and Kvicera and Pechac, for a MIMO, and for a single‐input‐multiple‐output LMS channel, respectively.…”

Section: Introductionmentioning

confidence: 99%

Statistical characterization of an urban dual‐polarized MIMO LMS channel

Nikolaidis

Moraitis

Kanatas

2018

Satell Commun Network

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This paper presents a thorough statistical characterization of a dual-polarized multiple-input-multiple-output channel, based on a measurement campaign of 2 land mobile satellite link scenarios in urban environments. The received signal is decomposed into its large-scale and small-scale fading parts, which are separately evaluated and characterized. The large-scale fading can be well approximated by the Lognormal distribution, whereas the small-scale fading in line-of-sight condition can be characterized as a Rician channel with a strong direct component and high Kfactors. On the other hand, in nonline-of-sight cases, the fading can be approximated by Nakagami or Rician distribution with low K-factors. The cross-polar discrimination is found between 14.2 and 22.5 dB, whereas the cross-polar isolation is found between 12.8 and 21 dB, respectively. Finally, assessing the diversity performance, applying the maximal ratio combining technique, it is found that the left-hand circular polarized transmission outperforms the right-hand circular polarized transmission, providing gains up to 2.5 dB, especially in nonline-of-sight cases. The low-diversity gains indicate that beamforming would be preferable to be applied in the specific channel.KEYWORDS channel characterization, channel modeling, dual-polarized, multiple-input-multiple-output (MIMO) satellite measurements | INTRODUCTIONIn the years to come, new narrowband satellite communication technologies will allow for direct connection of millions of Internet of Things (IoT) devices in a resource efficient way. The ultimate success of global IoT coverage will depend on the active support of satellite networks providing L-band and S-band services. Satellite technology serves as a key enabler to transform IoT connectivity across industries and geographical borders and has the potential to play a key role in cases where sensors and actuators are distributed over a very wide area as well as in remote areas not served by terrestrial access networks. Indeed, the use of the satellite is found to be of great importance in some applications of IoT, like smart grid, environmental monitoring, and emergency management, because satellite communications have unique merits such as large-scale coverage, superb ability to support emerging communications services, and cost effectiveness for broadcast/multicast connectivity. 1-3 Bringing wide-area connectivity for the IoT by using satellite technology is therefore an attractive solution to complement terrestrial networks, allowing densification and coverage extension in remote areas. 4The proliferation of multiantenna technology and its adoption in terrestrial communication systems provided unprecedented capacity improvement and enhanced spectral efficiency. The applicability of multiple-input-multiple-output (MIMO) techniques is also an appealing solution in satellite communication systems with a very promising outlook. 5,6 Specifically, land mobile satellite (LMS) systems can take advantage of MIMO techniques and achieve substantially in...

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Section: Introductionmentioning

confidence: 99%