Stochastic Power Control for Time-Varying Long-Term Fading Wireless Networks
<p/> <p>A new time-varying (TV) long-term fading (LTF) channel model which captures both the space and time variations of wireless systems is developed. The proposed TV LTF model is based on a stochastic differential equation driven by Brownian motion. This model is more realistic than t...
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2006-01-01
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Series: | EURASIP Journal on Advances in Signal Processing |
Online Access: | http://dx.doi.org/10.1155/ASP/2006/89864 |
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doaj-243a60f83db94672a168118f1832d4d22020-11-24T23:07:38ZengSpringerOpenEURASIP Journal on Advances in Signal Processing1687-61721687-61802006-01-0120061089864Stochastic Power Control for Time-Varying Long-Term Fading Wireless NetworksCharalambous Charalambos DOlama Mohammed MDjouadi Seddik M<p/> <p>A new time-varying (TV) long-term fading (LTF) channel model which captures both the space and time variations of wireless systems is developed. The proposed TV LTF model is based on a stochastic differential equation driven by Brownian motion. This model is more realistic than the static models usually encountered in the literature. It allows viewing the wireless channel as a dynamical system, thus enabling well-developed tools of adaptive and nonadaptive estimation and identification techniques to be applied to this class of problems. In contrast with the traditional models, the statistics of the proposed model are shown to be TV, but converge in steady state to their static counterparts. Moreover, optimal power control algorithms (PCAs) based on the new model are proposed. A centralized PCA is shown to reduce to a simple linear programming problem if predictable power control strategies (PPCS) are used. In addition, an iterative distributed stochastic PCA is used to solve for the optimization problem using stochastic approximations. The latter solely requires each mobile to know its received signal-to-interference ratio. Generalizations of the power control problem based on convex optimization techniques are provided if PPCS are not assumed. Numerical results show that there are potentially large gains to be achieved by using TV stochastic models, and the distributed stochastic PCA provides better power stability and consumption than the distributed deterministic PCA.</p> http://dx.doi.org/10.1155/ASP/2006/89864 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Charalambous Charalambos D Olama Mohammed M Djouadi Seddik M |
spellingShingle |
Charalambous Charalambos D Olama Mohammed M Djouadi Seddik M Stochastic Power Control for Time-Varying Long-Term Fading Wireless Networks EURASIP Journal on Advances in Signal Processing |
author_facet |
Charalambous Charalambos D Olama Mohammed M Djouadi Seddik M |
author_sort |
Charalambous Charalambos D |
title |
Stochastic Power Control for Time-Varying Long-Term Fading Wireless Networks |
title_short |
Stochastic Power Control for Time-Varying Long-Term Fading Wireless Networks |
title_full |
Stochastic Power Control for Time-Varying Long-Term Fading Wireless Networks |
title_fullStr |
Stochastic Power Control for Time-Varying Long-Term Fading Wireless Networks |
title_full_unstemmed |
Stochastic Power Control for Time-Varying Long-Term Fading Wireless Networks |
title_sort |
stochastic power control for time-varying long-term fading wireless networks |
publisher |
SpringerOpen |
series |
EURASIP Journal on Advances in Signal Processing |
issn |
1687-6172 1687-6180 |
publishDate |
2006-01-01 |
description |
<p/> <p>A new time-varying (TV) long-term fading (LTF) channel model which captures both the space and time variations of wireless systems is developed. The proposed TV LTF model is based on a stochastic differential equation driven by Brownian motion. This model is more realistic than the static models usually encountered in the literature. It allows viewing the wireless channel as a dynamical system, thus enabling well-developed tools of adaptive and nonadaptive estimation and identification techniques to be applied to this class of problems. In contrast with the traditional models, the statistics of the proposed model are shown to be TV, but converge in steady state to their static counterparts. Moreover, optimal power control algorithms (PCAs) based on the new model are proposed. A centralized PCA is shown to reduce to a simple linear programming problem if predictable power control strategies (PPCS) are used. In addition, an iterative distributed stochastic PCA is used to solve for the optimization problem using stochastic approximations. The latter solely requires each mobile to know its received signal-to-interference ratio. Generalizations of the power control problem based on convex optimization techniques are provided if PPCS are not assumed. Numerical results show that there are potentially large gains to be achieved by using TV stochastic models, and the distributed stochastic PCA provides better power stability and consumption than the distributed deterministic PCA.</p> |
url |
http://dx.doi.org/10.1155/ASP/2006/89864 |
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