SUMMARYTo develop and test real-time fade mitigation techniques control algorithms, propagation time series are needed. An alternative to using real data collected from propagation experiments is to generate typical fading time-series making use of climatological characteristics as well as geometrical and radio-electrical parameters of the link. The objective of this paper is to present a rain attenuation time-series synthesizer and able to generate events on demand. The model is based on an enhanced version of the Maseng-Bakken stochastic model. In the first part of this paper, the basic principles of the enhanced Maseng-Bakken model are recalled and the parameterization of this channel model is discussed for temperate European climates. Then, the theoretical bases of the Lacoste-Carrie 'event-on-demand' model and its validation constitute the second part of this paper. The enhanced Maseng-Bakken model is fully stochastic, whereas the Lacoste-Carrie 'event-on-demand' one offers the possibility to command the maximum attenuation level and the duration of the synthesized event.
In Recommendation ITU-R P.1853-1, a stochastic approach is proposed to generate long-term rain attenuation time series , including rain and no rain periods anywhere in the world. Nevertheless, its dynamic properties have been validated so far from experimental rain attenuation time series collected at mid-latitudes only. In the present paper, an effort is conducted to derive analytically the first-and second-order statistical properties of the ITU rain attenuation time-series synthesizer. It is then shown that the ITU synthesizer does not reproduce the first-order statistics (particularly the rain attenuation cumulative distribution function CDF), however, given as input parameters. It also prevents any rain attenuation correlation function other than exponential to be reproduced, which could be penalizing if a worldwide synthesizer that accounts for the local climatology has to be defined. Therefore, a new rain attenuation time-series synthesizer is proposed. It assumes a mixed Dirac-lognormal modeling of the absolute rain attenuation CDF and relies on a stochastic generation in the Fourier plane. It is then shown analytically that the new synthesizer reproduces much better the first-order statistics given as input parameters and enables any rain attenuation correlation function to be reproduced. The ability of each synthesizer to reproduce absolute rain attenuation CDFs given by Recommendation ITU-R P.618 is finally compared on a worldwide basis. It is then concluded that the new rain attenuation time-series synthesizer reproduces the rain attenuation CDF much better, preserves the rain attenuation dynamics of the current ITU synthesizer for simulations at mid-latitudes, and, if it proves to be necessary for worldwide applications, is able to reproduce any rain attenuation correlation function.
The well-known Space-Alternating Generalized Expectation Maximisation (SAGE) algorithm has been recently considered for multipath mitigation in Global Navigation Satellite System (GNSS) receivers. However, the implementation of SAGE in a GNSS receiver is a challenging issue due to the numerous number or parameters to be estimated and the important size of the data to be processed. A new implementation of the SAGE algorithm is proposed in this paper in order to reach the same efficiency with a reduced complexity. This paper focuses on the trade-off between complexity and performance thanks to the Cramer Rao bound derivation. Moreover, this paper shows how the proposed algorithm can be integrated with a classical GNSS tracking loop. This solution is thus a very promising approach for multipath mitigation.
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