Adaptive algorithms and stochastic approximations
Adaptive algorithms and stochastic approximations
Stochastic differential equations (3rd ed.): an introduction with applications
Stochastic differential equations (3rd ed.): an introduction with applications
Probability, stochastic processes, and queueing theory: the mathematics of computer performance modeling
Wireless Communications: Principles and Practice
Wireless Communications: Principles and Practice
Power-controlled matiple access schemes for next-generation wireless packet networks
IEEE Wireless Communications
IEEE Transactions on Wireless Communications
Decentralized dynamic power control for cellular CDMA systems
IEEE Transactions on Wireless Communications
Standard and quasi-standard stochastic power control algorithms
IEEE Transactions on Information Theory
Stochastic power control for wireless networks via SDEs: probabilistic QoS measures
IEEE Transactions on Information Theory
Analysis of an up/down power control algorithm for the CDMA reverse link under fading
IEEE Journal on Selected Areas in Communications
IEEE Journal on Selected Areas in Communications
A framework for uplink power control in cellular radio systems
IEEE Journal on Selected Areas in Communications
Recursive estimation and identification of time-varying long-term fading channel
Research Letters in Signal Processing
Long-term fading channel estimation from sample covariances
Automatica (Journal of IFAC)
IEEE Transactions on Wireless Communications
CA '07 Proceedings of the Ninth IASTED International Conference on Control and Applications
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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.