Improvement Of Hidden Markov model Evaluation Of the Mobile Satellite Channel by Resorting to a Transition Localisation Method

The mobile satellite channel has underlying Markovian properties and can then be represented by a Hidden Markov model (HMM). A challenging problem consists in estimating the model parameters from experimental data, especially when these parameters are not easily identifiable. In these cases, classification methods like k-means or scalable clustering, which are considered in this paper, show poor results when applied to the channel signal directly. We show that the detection of change-points of the signal, i.e. the detection of transitions between the model states, in a preliminary step, improves the estimation of the model parameters. We thus propose a method of model estimation including the detection of change-points that enables a better modelling of the satellite channel

Application of Monte Carlo Markov Chain to Determination of Hidden Markov Model for Mobile Satellite Channels

The fluctuations of mobile satellite channels are usually modelled by Markov chains. Existing models postulate the number of states, and their associated distributions based on physical considerations. This produces good models but that are not convenient in different contexts. In this paper, we focus on the methodology of extraction of Hidden Markov Model (HMM) from experimental data to describe the time fluctuations of received power in a mobile satellite service (MSS) context. It is based on a MCMC (Monte Carlo Markov Chain) method associated with a k-means classification. Its complexity is reduced when compared to traditional MCMC method. Contrary to existing detection methods, the only assumption is the HMM states number and it enables an accurate estimation of the HMM parameters and of the transitions location between states of the model

Modelling of the Markovian Behaviour of Mobile Satellite

Abstract — Classical models for mobile satellite channels are based on Markov chains where each state is linked to special propagation conditions. In this paper, we focus on the methodology of extraction of Hidden Markov Model (HMM) from experimental data to describe the time fluctuations of received power in a mobile satellite service (MSS) context. The method developed in this paper is based on a MCMC (Monte Carlo Markov Chain) training phase associated with a k-means classification. Its complexity is reduced when compared to traditional MCMC method. Contrary to existing detection methods, nearly no prior are necessary and it enables an accurate estimation of the HMM parameters and the possibility to detect the evolution of the channel sequentially. Furthermore, a sequential method, based on estimated HMM parameters, is presented and enables to follow the on-line variation of the state sequence. Keywords-component; satellite channel modelling, MCMC, HMM I

Accurate and Novel Modeling of the Land Mobile Satellite Channel using Reversible Jump Markov Chain Monte Carlo Technique

Abstract—A key issue in the design of a mobile satellite communication system is an adequate knowledge of the statistical behavior of the propagation channel. To achieve this goal, the development of very accurate models plays a very important role. In contrast to traditional multi-state Markov chain based models, the novel approach proposed in this paper makes no prior assumptions on the number of states or on the statistical distributions characterizing each state. The sequence of channel states is blindly estimated using a Reversible Jum