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Title: Wireless Fading Channel Models: From Classical to Stochastic Differential Equations

Abstract

The wireless communications channel constitutes the basic physical link between the transmitter and the receiver antennas. Its modeling has been and continues to be a tantalizing issue, while being one of the most fundamental components based on which transmitters and receivers are designed and optimized. The ultimate performance limits of any communication system are determined by the channel it operates in. Realistic channel models are thus of utmost importance for system design and testing. In addition to exponential power path-loss, wireless channels suffer from stochastic short term fading (STF) due to multipath, and stochastic long term fading (LTF) due to shadowing depending on the geographical area. STF corresponds to severe signal envelope fluctuations, and occurs in densely built-up areas filled with lots of objects like buildings, vehicles, etc. On the other hand, LTF corresponds to less severe mean signal envelope fluctuations, and occurs in sparsely populated or suburban areas. In general, LTF and STF are considered as superimposed and may be treated separately. Ossanna was the pioneer to characterize the statistical properties of the signal received by a mobile user, in terms of interference of incident and reflected waves. His model was better suited for describing fading occurring mainly inmore » suburban areas (LTF environments). It is described by the average power loss due to distance and power loss due to reflection of signals from surfaces, which when measured in dB's give rise to normal distributions, and this implies that the channel attenuation coefficient is log-normally distributed. Furthermore, in mobile communications, the LTF channel models are also characterized by their special correlation characteristics which have been reported. Clarke introduced the first comprehensive scattering model describing STF occurring mainly in urban areas. An easy way to simulate Clarke's model using a computer simulation is described. This model was later expanded to three-dimensions (3D) by Aulin. An indoor STF was introduced. Most of these STF models provide information on the frequency response of the channel, described by the Doppler power spectral density (DPSD). Aulin presented a methodology to find the Doppler power spectrum by computing the Fourier transform of the autocorrelation function of the channel impulse response with respect to time. A different approach, leading to the same Doppler power spectrum relation was presented by Gans. These STF models suggest various distributions for the received signal amplitude such as Rayleigh, Rician, or Nakagami. Models based on autoregressive and moving averages (AR) are proposed. However, these models assume that the channel state is completely observable, which in reality is not the case due to additive noise, and requires long observation intervals. First order Markov models for Raleigh fading have been proposed, and the usefulness of a finite-state Markov channel model is argued. Mobile-to-mobile (or ad hoc) wireless networks comprise nodes that freely and dynamically self-organize into arbitrary and/or temporary network topology without any fixed infrastructure support. They require direct communication between a mobile transmitter and a mobile receiver over a wireless medium. Such mobile-to-mobile communication systems differ from the conventional cellular systems, where one terminal, the base station, is stationary, and only the mobile station is moving. As a consequence, the statistical properties of mobile-to-mobile links are different from cellular ones. Copious ad hoc networking research exists on layers in the open system interconnection (OSI) model above the physical layer. However, neglecting the physical layer while modeling wireless environment is error prone and should be considered more carefully. The experimental results show that the factors at the physical layer not only affect the absolute performance of a protocol, but because their impact on different protocols is nonuniform, it can even change the relative ranking among protocols for the same scenario. The importance of the physical layer is demonstrated by evaluating the Medium Access Control (MAC) performance. Most of the research conducted on wireless channel modeling deals mainly with deterministic wireless channel models. In these models, the speeds of the nodes are assumed to be constant and the statistical characteristics of the received signal are assumed to be fixed with time. But in reality, the propagation environment varies continuously due to mobility of the nodes at variable speeds and movement of objects or scatter across transmitters and receivers resulting in appearance or disappearance of existing paths from one instant to the next. As a result, the current models that assume fixed statistics are unable to capture and track complex time variations in the propagation environment.« less

Authors:
 [1];  [1];  [2]
  1. ORNL
  2. University of Cyprus
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
OE USDOE - Office of Electric Transmission and Distribution
OSTI Identifier:
1008825
DOE Contract Number:  
DE-AC05-00OR22725
Resource Type:
Book
Country of Publication:
United States
Language:
English
Subject:
99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE; ANTENNAS; COMMUNICATIONS; COMPUTERIZED SIMULATION; DESIGN; DIFFERENTIAL EQUATIONS; PERFORMANCE; TESTING; ENERGY SPECTRA; MATHEMATICAL MODELS

Citation Formats

Olama, Mohammed M, Djouadi, Seddik M, and Charalambous, Prof. Charalambos. Wireless Fading Channel Models: From Classical to Stochastic Differential Equations. United States: N. p., 2010. Web.
Olama, Mohammed M, Djouadi, Seddik M, & Charalambous, Prof. Charalambos. Wireless Fading Channel Models: From Classical to Stochastic Differential Equations. United States.
Olama, Mohammed M, Djouadi, Seddik M, and Charalambous, Prof. Charalambos. Fri . "Wireless Fading Channel Models: From Classical to Stochastic Differential Equations". United States.
@article{osti_1008825,
title = {Wireless Fading Channel Models: From Classical to Stochastic Differential Equations},
author = {Olama, Mohammed M and Djouadi, Seddik M and Charalambous, Prof. Charalambos},
abstractNote = {The wireless communications channel constitutes the basic physical link between the transmitter and the receiver antennas. Its modeling has been and continues to be a tantalizing issue, while being one of the most fundamental components based on which transmitters and receivers are designed and optimized. The ultimate performance limits of any communication system are determined by the channel it operates in. Realistic channel models are thus of utmost importance for system design and testing. In addition to exponential power path-loss, wireless channels suffer from stochastic short term fading (STF) due to multipath, and stochastic long term fading (LTF) due to shadowing depending on the geographical area. STF corresponds to severe signal envelope fluctuations, and occurs in densely built-up areas filled with lots of objects like buildings, vehicles, etc. On the other hand, LTF corresponds to less severe mean signal envelope fluctuations, and occurs in sparsely populated or suburban areas. In general, LTF and STF are considered as superimposed and may be treated separately. Ossanna was the pioneer to characterize the statistical properties of the signal received by a mobile user, in terms of interference of incident and reflected waves. His model was better suited for describing fading occurring mainly in suburban areas (LTF environments). It is described by the average power loss due to distance and power loss due to reflection of signals from surfaces, which when measured in dB's give rise to normal distributions, and this implies that the channel attenuation coefficient is log-normally distributed. Furthermore, in mobile communications, the LTF channel models are also characterized by their special correlation characteristics which have been reported. Clarke introduced the first comprehensive scattering model describing STF occurring mainly in urban areas. An easy way to simulate Clarke's model using a computer simulation is described. This model was later expanded to three-dimensions (3D) by Aulin. An indoor STF was introduced. Most of these STF models provide information on the frequency response of the channel, described by the Doppler power spectral density (DPSD). Aulin presented a methodology to find the Doppler power spectrum by computing the Fourier transform of the autocorrelation function of the channel impulse response with respect to time. A different approach, leading to the same Doppler power spectrum relation was presented by Gans. These STF models suggest various distributions for the received signal amplitude such as Rayleigh, Rician, or Nakagami. Models based on autoregressive and moving averages (AR) are proposed. However, these models assume that the channel state is completely observable, which in reality is not the case due to additive noise, and requires long observation intervals. First order Markov models for Raleigh fading have been proposed, and the usefulness of a finite-state Markov channel model is argued. Mobile-to-mobile (or ad hoc) wireless networks comprise nodes that freely and dynamically self-organize into arbitrary and/or temporary network topology without any fixed infrastructure support. They require direct communication between a mobile transmitter and a mobile receiver over a wireless medium. Such mobile-to-mobile communication systems differ from the conventional cellular systems, where one terminal, the base station, is stationary, and only the mobile station is moving. As a consequence, the statistical properties of mobile-to-mobile links are different from cellular ones. Copious ad hoc networking research exists on layers in the open system interconnection (OSI) model above the physical layer. However, neglecting the physical layer while modeling wireless environment is error prone and should be considered more carefully. The experimental results show that the factors at the physical layer not only affect the absolute performance of a protocol, but because their impact on different protocols is nonuniform, it can even change the relative ranking among protocols for the same scenario. The importance of the physical layer is demonstrated by evaluating the Medium Access Control (MAC) performance. Most of the research conducted on wireless channel modeling deals mainly with deterministic wireless channel models. In these models, the speeds of the nodes are assumed to be constant and the statistical characteristics of the received signal are assumed to be fixed with time. But in reality, the propagation environment varies continuously due to mobility of the nodes at variable speeds and movement of objects or scatter across transmitters and receivers resulting in appearance or disappearance of existing paths from one instant to the next. As a result, the current models that assume fixed statistics are unable to capture and track complex time variations in the propagation environment.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2010},
month = {1}
}

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