An algorithm is proposed that computes the solution to the power control problem with closed-loop effects (L. Mendo and J.M. Hernando, 2007). Analytical and simulation results show that the algorithm converges under the same conditions as that given in the cited reference, while achieving a reduction in computational load in a wide range of conditions.

This letter presents an improved result on the negative-binomial Monte Carlo technique analyzed in a previous paper1 for the estimation of an unknown probability p. Specifically, the confidence level associated to a relative interval [p/μ2, pμ1], with μ1, μ2 > 1, is proved to exceed its asymptotic value for a broader range of intervals than that given in the referred paper, and for any value of p. This extends the applicability of the estimator, relaxing the conditions that guarantee a given confidence level.

A system-level model for power control in the uplink of direct-sequence code-division multiple access (DS-CDMA) cellular networks is presented. The model takes into account the effects of the closed loop, namely average transmit power rise, power headroom, and variable soft hand-off gains, which are not considered in existing power control models. The power control problem is analyzed in this setting, and a number of properties are derived which generalize results from the classical model without closed-loop effects. Based on this analysis, an algorithm is given that determines required transmit powers taking into account these effects. This algorithm is a generalization of classical iterative algorithms without closed-loop effects. Finally, simulation results are presented to assess the validity of the approach

In this paper, a sequential stopping rule for the estimation of a probability p by means of Monte Carlo simulation is analyzed. It is shown that the proposed estimator is almost unbiased, and guarantees a given relative precision irrespective of p. Under very mild conditions, the method also guarantees a certain confidence level for a given relative estimation error, provided that p does not exceed a maximum value. An extension to importance sampling is discussed.

An algorithm is proposed that finds the optimum assignment of mobile users to base stations, and its associated transmission powers, in a cellular direct-sequence code-division multiple-access network, with a computational complexity that grows polynomially with the number of users and base stations. The algorithm detects infeasible situations and allows the inclusion of power constraints. Its performance is analyzed in terms of complexity and system capacity.

We show how the dimension of power control equation systems can be reduced from K, the number of users in the system, to M, the number of cells, without any loss of generality or accuracy. Decentralized downlink power control algorithms are then presented which generalize previously proposed ones broadening the range of application while maintaining reduced complexity.