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Stochastic Power Control for Cellular Radio Systems
 IEEE Trans. Commun
, 1997
"... For wireless communication systems, iterative power control algorithms have been proposed to minimize transmitter powers while maintaining reliable communication between mobiles and base stations. To derive deterministic convergence results, these algorithms require perfect measurements of one or mo ..."
Abstract

Cited by 89 (8 self)
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For wireless communication systems, iterative power control algorithms have been proposed to minimize transmitter powers while maintaining reliable communication between mobiles and base stations. To derive deterministic convergence results, these algorithms require perfect measurements of one or more of the following parameters: (i) the mobile's signal to interference ratio (SIR) at the receiver, (ii) the interference experienced by the mobile, and (iii) the bit error rate. However, these quantities are often difficult to measure and deterministic convergence results neglect the effect of stochastic measurements. In this work, we develop distributed iterative power control algorithms that use readily available measurements. Two classes of power control algorithms are proposed. Since the measurements are random, the proposed algorithms evolve stochastically and we define the convergence in terms of the mean squared error (MSE) of the power vector from the optimal power vector that is t...
Adaptive power control and MMSE interference suppression
 Wireless Networks
, 1998
"... Power control algorithms assume that the receiver structure is fixed and iteratively update the transmit powers of the users to provide acceptable quality of service while minimizing the total transmitter power. Multiuser detection, on the other hand, optimizes the receiver structure with the assump ..."
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Cited by 37 (4 self)
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Power control algorithms assume that the receiver structure is fixed and iteratively update the transmit powers of the users to provide acceptable quality of service while minimizing the total transmitter power. Multiuser detection, on the other hand, optimizes the receiver structure with the assumption that the users have fixed transmitter powers. In this study, we combine the two approaches and propose an iterative and distributed power control algorithm which iteratively updates the transmitter powers and receiver filter coefficients of the users. We show that the algorithm converges to a minimum power solution for the powers, and an MMSE multiuser detector for the filter coefficients. 1.
Stochastic learning solution for distributed discrete power control game in wireless data networks
, 2008
"... Distributed power control is an important issue in wireless networks. Recently, noncooperative game theory has been applied to investigate interesting solutions to this problem. The majority of these studies assumes that the transmitter power level can take values in a continuous domain. However, r ..."
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Cited by 15 (0 self)
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Distributed power control is an important issue in wireless networks. Recently, noncooperative game theory has been applied to investigate interesting solutions to this problem. The majority of these studies assumes that the transmitter power level can take values in a continuous domain. However, recent trends such as the GSM standard and Qualcommâ€™s proposal to the IS95 standard use a finite number of discretized power levels. This motivates the need to investigate solutions for distributed discrete power control which is the primary objective of this paper. We first note that, by simply discretizing, the previously proposed continuous power adaptation techniques will not suffice. This is because a simple discretization does not guarantee convergence and uniqueness. We propose two probabilistic power adaptation algorithms and analyze their theoretical properties along with the numerical behavior. The distributed discrete power control problem is formulated as anperson, nonzero sum game. In this game, each user evaluates a power strategy by computing a utility value. This evaluation is performed using a stochastic iterative procedures. We approximate the discrete power control iterations by an equivalent ordinary differential equation to prove that the proposed stochastic learning power control algorithm converges to a stable Nash equilibrium. Conditions when more than one stable Nash equilibrium or even only mixed equilibrium may exist are also studied. Experimental results are presented for several cases and compared with the continuous power level adaptation solutions.
Optimal Power Control in CDMA Networks with Constraints on Outage Probability
 in Proc. WiOptâ€™03, INRIA Sophia Antipolis
, 2003
"... This paper proposes a new scheme that couples power control with a minimum outage probability multiuser detector. The resultant iterative algorithm is conceptually simple and finds the minimum sum power of all users with a set of outage probability constraints. Bounds on the outage probability expre ..."
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Cited by 2 (1 self)
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This paper proposes a new scheme that couples power control with a minimum outage probability multiuser detector. The resultant iterative algorithm is conceptually simple and finds the minimum sum power of all users with a set of outage probability constraints. Bounds on the outage probability expression are found that extend a previous result that did not include receiver noise. These bounds are used to create a suboptimal scheme coupling power control and a MMSE multiuser detector. This new problem becomes a variant of an existing problem where outage probability constraints are first mapped to average SIR threshold constraints. Using a recent result that transforms complex SIR expressions into a compact and decoupled form, a noniterative and computationally inexpensive power control algorithm is developed for large systems of users. Simulation results are presented showing the closeness of the optimal and mapped schemes, speed of convergence and performance comparisons with other standard receivers.