State modelling of the land mobile propagation channel for dual-satellite systems

Abstract

The quality of service of mobile satellite reception can be improved by using multi-satellite diversity (angle diversity). The recently finalised MiLADY project targeted therefore on the evaluation and modelling of the multi-satellite propagation channel for land mobile users with focus on broadcasting applications. The narrowband model combines the parameters from two measurement campaigns: In the U.S. the power levels of the Satellite Digital Audio Radio Services were recorded with a high sample rate to analyse fast and slow fading effects in great detail. In a complementary campaign signals of Global Navigation Satellite Systems (GNSS) were analysed to obtain information about the slow fading correlation for almost any satellite constellation. The new channel model can be used to generate time series for various satellite constellations in different environments. This article focuses on realistic state sequence modelling for angle diversity, confining on two satellites. For this purpose, different state modelling methods providing a joint generation of the states 'good good', 'good bad', 'bad good' and 'bad bad' are compared. Measurements and re-simulated data are analysed for various elevation combinations and azimuth separations in terms of the state probabilities, state duration statistics, and correlation coefficients. The finally proposed state model is based on semi-Markov chains assuming a log-normal state duration distribution.

Estimation of fading parameter correlation for modeling the land mobile satellite channel

“…(MA (n) , ΣA (n) , MP (n) )Sat1 (MA (2) , ΣA (2) , MP (2) )Sat1 states Sat 1 states Sat 2 (MA (2) , ΣA (2) , MP (2) )Sat2 (MA (n) , ΣA (n) , MP (n) )Sat2 (MA (1) , ΣA (1) , MP (1) )Sat2…”

“…Within each of the states in the sequence, slow variations (caused by varying shadowing conditions and diffraction effects) and fast variations (due to multipath propagation) are superimposed. To model the very slow variations, Markov chains can be used [1]. For the slow and fast variations various models are found in the literature [2] whereas Loo's distribution [3] is widely used.…”

Influence of driving direction on Land Mobile Satellite channels

“…In this contribution we consider two different approaches: The first method does not take the driving direction into account, which is the result of the currently used LMS channel models, e.g. [7]. The bad state probability of each satellite (P b1 and P b2 ) is a function of the environment env and the corresponding elevations φ 1 , φ 2 .…”

Cognitive dual satellite systems

“…A number of works do focus on the signal/channel model for the LMS-DSS scenario [19,20,48,49]. A possible reason for this is that FSS-DSS system models presented in Section 12.3.2 could be directly applied to the LMS-DSS case.…”

“…When having two satellites, users with a blocked line of sight to one of the satellites can link to the other satellite obtaining and uninterrupted service [48]. network are sent to two gateways which will forward it to its associated satellite.…”