Stochastic Modeling and Power Control of Time-Varying Wireless Communication Networks

Mohammed M. Olama, Seddik M. Djouadi, Charalambos D. Charalambous

Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, USA.
Department of Electrical and Computer Engineering, University of Cyprus, Nicosia, Cyprus.
Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, USA.

DOI: 10.4236/cn.2014.63017                

Abstract

Wireless networks are characterized by nodes mobility, which makes the propagation environment time-varying and subject to fading. As a consequence, the statistical characteristics of the received signal vary continuously, giving rise to a Doppler power spectral density (DPSD) that varies from one observation instant to the next. This paper is concerned with dynamical modeling of time-varying wireless fading channels, their estimation and parameter identification, and optimal power control from received signal measurement data. The wireless channel is characterized using a stochastic state-space form and derived by approximating the time-varying DPSD of the channel. The expected maximization and Kalman filter are employed to recursively identify and estimate the channel parameters and states, respectively, from online received signal strength measured data. Moreover, we investigate a centralized optimal power control algorithm based on predictable strategies and employing the estimated channel parameters and states. The proposed models together with the estimation and power control algorithms are tested using experimental measurement data and the results are presented.

Keywords

Wireless Networks, Time-Varying Wireless Fading Channel, Impulse Response, Doppler Power Spectral Density, Stochastic State-Space Model, Stochastic Modeling, Optimal Power Control, Expectation Maximization, Kalman Filter

Conflicts of Interest

The authors declare no conflicts of interest.

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