Fast stochastic power control algorithms for nonlinear multiuser receivers

Abstract

Uplink communication in a cellular radio network is considered where the base station in each cell employs linear or nonlinear (decision feedback) multiuser receivers. For any such receiver, the problem of interest is that of minimizing the total transmit power under the constraint that all the users of the network achieve their quality-of-service objective in terms of signal-to-interference ratio (SIR). When the solution is feasible for the desired SIR requirements, the optimum powers are computed with a distributed iterative power control strategy suitable for implementation at each base station. While the deterministic algorithm requires both in-cell and out-of-cell user information, the stochastic algorithm proposed in this paper can be implemented at the base stations in a truly distributed manner requiring knowledge of only in-cell parameters. Such an algorithm was proposed recently for the case where base stations use linear (single user) matched filter (MF) receivers. However, the feasibility region in terms of attainable SIRs for a well-designed multiuser receiver, particularly for a nonlinear receiver that employs decision feedback, is generally much larger than it is for the linear MF receiver. The stochastic power control algorithm in this paper, for linear or nonlinear multiuser receivers, converges in the mean-square sense to the minimal powers when the target SIRs are feasible. The second major focus of this paper is to improve the convergence properties of the conventional stochastic approximation based power control strategy by using the more recent results on averaging. Convergence issues of both the "nonaveraged" and "averaged" algorithms are investigated, and numerical examples are presented to demonstrate the performance improvement due to averaging.

Standard and Quasi-Standard Stochastic Power Control Algorithms

“…Under the assumption of conventional matched filter receivers, [8] showed that the stochastic PC, which updates the power vector iteratively using stochastic approximations, converges to the optimal power vector in the mean square error (MSE) sense as long as the step-size sequence in the iteration meets certain requirements. These results were later extended to the cases when a linear receiver or a decision feedback receiver is used [9], [10]. A fast PC algorithm with averaging was also proposed in [10].…”

“…These results were later extended to the cases when a linear receiver or a decision feedback receiver is used [9], [10]. A fast PC algorithm with averaging was also proposed in [10]. The common feature of [8] and [10] is that, for both cases, the deterministic interference function is linear in transmit powers.…”

“…A fast PC algorithm with averaging was also proposed in [10]. The common feature of [8] and [10] is that, for both cases, the deterministic interference function is linear in transmit powers. Among the iterative PC algorithms, the joint PC and receiver optimization algorithm also attracted special attention.…”

“…The convergence of the deterministic version was studied in [11] and the stochastic implementation was studied in [12], where a local convergence was shown for the stochastic PC algorithm given that the step size sequence satisfies certain requirements. Unlike the deterministic iterative PC, where the convergence was proved for a general class of algorithms [7], a common feature on the convergence results for stochastic PC in [8], [12], 0018-9448/$20.00 © 2005 IEEE [10] is that they assume specific closed-form expressions for the interference function.…”

“…The stochastic PC algorithms proposed in [8], [12], [10] are based on stochastic approximation methods, which were introduced in [13] and [14]. Since then, stochastic approximation-based algorithms have been studied extensively in many different contexts including communications, signal processing, and neural networks.…”