Discrete Rayleigh fading channel modeling

Abstract

In order to understand the behaviour of upper-layer protocols and to design or fine tune their parameters over wireless networks, it is common to assume that the underlying channel is a flat Rayleigh fading channel. Such channels are commonly modeled as finite state Markov chains. Recently, hidden Markov models have also been employed to characterize these channels. In this paper, we study the different models that have been proposed along with the analysis of their validity. We start by presenting some preliminary concepts related to the modeling of the wireless communications channel. We then proceed to introduce finite state Markov channel models (FSMCs) along with the relations between them and the modulation schemes, error control protocols and channel coding. We propose and study the effects of taking into account the fading process in its characterization. We finish with a discussion on hidden Markov models for Rayleigh fading channel modeling.

Cross-layer modeling of wireless channels for data-link and IP layer performance evaluation

“…In general, B T must be estimated based on a modulation scheme, type of the receiver and other specific features utilized at a given wireless channel [9]. Unfortunately, analytical expressions for (1) are available for simple modulation schemes only [6,14]. Therefore, models of the SNR process and the received signal strength process are not convenient for performance evaluation purposes requiring extensive field measurements of empirical relationship between the SNR value and the bit error probability.…”

“…modulated by the discrete-time Markov chain {S L (n), nZ0, 1,. }, S L (n)Z{1,2,.,M}, each state of which is associated with conditional probability distribution function of the received signal strength [1,2,6]. The underlying modulation allows to take into account autocorrelation properties of the signal strength process.…”

“…The small-scale propagation models capture characteristics of wireless channel on a finer granularity than largescale ones [1,2,6,13]. Additionally, these models implicitly take into account movement of users over short travel distances [9].…”

“…To predict performance of information transmission over wireless channels, models representing the channel state in terms of signal-to-noise ratio (SNR) or bit errors are often used (see [1][2][3][4][5] among others and [6,7] for review). However, those models cannot be directly used in performance evaluation studies and must be properly extended to the data-link and IP layers at which QoS parameters are usually defined.…”

Modeling and simulation of wireless link quality (ETT) through principal component analysis of trace data

“…However there are other approaches that have been employed for analyzing time varying data. For example, in wireless communications, the time varying nature of the envelope of the received signal (which exhibits Rayleigh fading) has been modeled as a Markov process or a hidden Markov process with two or more states (see for example [8]). The assumption of the future state being dependent only on the current state could be problematic or perhaps not significant (similar to our assumptions of independence).…”