A context-tree based model for quantized fading

Babich, F.; Kelly, O.E.; Lombardi, G.

doi:10.1109/4234.749358

Cited by 22 publications

(18 citation statements)

References 6 publications

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“…In this case, a longer sequence of previous fading channel gains are required to represent channel's evolution in time. The authors in [17], [49] also mention that high-order FSMC models may be more useful in applications related to analyzing packet error behavior or channel statistics (such as the ACF), whereas for wireless protocol evaluation low-order FSMC model may suffice [17]. The requirement of having high-order FSMC models for accurate representation of fading channel statistics and packet error behavior was later confirmed by [19] for f D T s < 0.1.…”

Section: The Effect Of Markov Memory Ordermentioning

confidence: 88%

“…CTP is the third technique for obtaining higher-order FSMC models for fading channels, proposed in [17] and [49]. Let us first assume that a sample-by-sample quantization of the fading channel gain sequence of length N is available and the discrete-valued fading channel gain sequence is denoted by u N 1 , The CTP method is an algorithm to estimate the contexts from the training sequence u N 1 and to simplify the full tree by merging contexts.…”

Section: Ctp Methodsmentioning

confidence: 99%

“…For Rayleigh fading, the pdf f A (a) was given in (2). Other statistical models used for the FSMC modeling of the fading amplitude include Rician [16], [17], [19], [49] and Nakagami m [23], [24]. Having an analytical expression for f A (a) is not necessary for obtaining FSMC steady-state probabilities.…”

Section: First-order Fsmc Models For the Tv-ffc Amplitude Or Receivedmentioning

confidence: 99%

See 2 more Smart Citations

Finite-state Markov modeling of fading channels - a survey of principles and applications

Sadeghi

Kennedy

Rapajic

et al. 2008

IEEE Signal Process. Mag.

429

313

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In late 1950s and early 1960s, Gilbert and Elliott at Bell Labs were modeling burst-noise telephone circuits with a very simple two-state channel model with memory. This simple model allowed them to evaluate channel capacity and error rate performance through bursty wireline telephone circuits. However, it took another 30 years for the so-called Gilbert-Elliott channel (GEC) and its generalized finite-state Markov channel (FSMC) to be applied in the design of second-generation (2G) wireless communication systems. Since the mid 1990s, the GEC and FSMC models have been widely used for modeling wireless flat-fading channels in a variety of applications, ranging from modeling channel error bursts to decoding at the receiver. FSMC models are versatile, and with suitable choices of model parameters, can capture the essence of time-varying fading channels. This article's goal is to provide an in-depth understanding of the principles of FSMC modeling of fading channels with its applications in wireless communication systems. Digital Object Identifier 10.1109/MSP.2008 [ Parastoo Sadeghi, Rodney A. Kennedy, Predrag B. Rapajic, and Ramtin Shams ] While the emphasis is on frequency nonselective or flat-fading channels, this understanding will be useful for future generalizations of FSMC models for frequency-selective fading channels. The target audience of this article include both theory-and practice-oriented researchers who would like to design accurate channel models for evaluating the performance of wireless communication systems in the physical or media access control layers, or those who would like to develop more efficient and reliable transceivers that take advantage of the inherent memory in fading channels. Both FSMC models and flat-fading channels will be formally introduced. However, a background in time-varying fading communication channels is beneficial.We consider the FSMC modeling of fading channels from five distinct viewpoints. First, we provide a brief history of FSMC models and the FSMC modeling of flat-fading channels. Second, we define the parameters of FSMC models and discuss how these parameters can be derived from flat-fading channel statistics. We point out the trade-offs between model accuracy and complexity. Third, we categorize applications of FSMC models for fading channels into four categories and discuss the FSMC model applicability and accuracy in each application. We pay special attention to the effect of FSMC memory order on the model accuracy. Fourth, we consider the information-theoretical aspects of FSMC models and the FSMC modeling of fading channels. Finally, we present open questions and directions for future research. For easier access to the technical contents, the reader can refer to the "List of Acronyms" and "Notational Conventions." THE HISTORY OF FSMC 1957-1968: DEVELOPMENT OF FSMC MODELSThe study of finite-state communication channels with memory dates back to the work by Shannon in 1957 [1]. In [1], Shannon proved coding theorems for finite-state channels (FSCs) with discret...

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Section: The Effect Of Markov Memory Ordermentioning

confidence: 88%

Section: Ctp Methodsmentioning

confidence: 99%

Section: First-order Fsmc Models For the Tv-ffc Amplitude Or Receivedmentioning

confidence: 99%

See 1 more Smart Citation

Finite-state Markov modeling of fading channels - a survey of principles and applications

Sadeghi

Kennedy

Rapajic

et al. 2008

IEEE Signal Process. Mag.

429

313

View full text Add to dashboard Cite

show abstract

“…Under fast fading conditions ( f m Â > 0.4), an uncorrelated model (zero-order model) proved to adequately approximate the fading process. For intermediate values of fading, Babich et al [7,8] suggested a higher order model. Nevertheless, this literature does not elaborate extensively on the selection process of the number of states.…”

Section: Number Of States and Model Ordermentioning

confidence: 99%

Discrete Rayleigh fading channel modeling

Aráuz

Krishnamurthy

Labrador

2004

Wireless Communications

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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.

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“…Joint iterative channel estimation and data detection is readily applicable with FSMC modelling of the fading channel [8], [10]. Modelling the fading channel evolution in time with the optimum, finite channel memory order is addressed in [11], [12]. The results indicate that the first-order Markovian assumption is accurately applicable for channel normalized fading rates of f D T 0.01, where f D is the maximum Doppler frequency and T is the symbol period.…”

Section: Introductionmentioning

confidence: 99%

Analysis of pilot symbol assisted modulation in fading channels using finite state Markov models

Sadeghi

Rapajic

Eighth IEEE International Symposium on Spread Spectrum Techniques and Applications - Programme and Book of Abstracts (IEEE Cat.

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Abstract-In this paper, we present an analysis for the channel state estimation and coherent data detection in pilot symbol assisted modulation (PSAM), which is based on the maximum a posteriori (MAP) criterion. The time varying flat fading channel is modelled as the finite state Markov channel (FSMC). The exact channel state estimation error probability is analyzed, using the forward recursion in the Forward-Backward algorithm. The effect of channel state estimation error probability on the receiver symbol error rate (SER) performance in M-ary phase shift keying (PSK) scheme is formulated. The analysis is applied for different channel phase quantization levels and various pilot symbol numbers. The results indicate that BPSK SER performance is saturated with more than 8 to 16 levels of channel phase quantization. For QPSK scheme, 16 levels of channel phase quantization is adequate. Moreover, the transmission of 3 consecutive pilot symbols achieves an acceptable SER performance.

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A context-tree based model for quantized fading

Cited by 22 publications

References 6 publications

Finite-state Markov modeling of fading channels - a survey of principles and applications

Finite-state Markov modeling of fading channels - a survey of principles and applications

Discrete Rayleigh fading channel modeling

Analysis of pilot symbol assisted modulation in fading channels using finite state Markov models

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