A new choice of penalty function for robust multiuser detection based on M-estimation

In this letter, we propose a new robust MUD, called /spl alpha/ detector, for non-Gaussian noise. We consider the Gaussian-mixture model for non-Gaussian or impulsive noise. Our technique outperforms the decorrelator and the minimax detectors in highly impulsive noise. The proposed method uses a parametric cost function, where the parameter /spl alpha/ is selected using the difference between the asymptotic variance of estimation error of the /spl alpha/ detector and that of the minimax detector.     

Signed-rank nonparametric multiuser detection in non-Gaussian channels

We present a novel nonparametric multiuser detector for non-Gaussian channels that is based on the signed-rank norm for linear regression. Analytical and simulation results show that the proposed detector offers similar or better performance as compared to the minimax robust detector, but without requiring any a priori information on the noise. The complexity of this detector is lower than that of the pseudo-norm nonparametric detector stated previously by the authors. This is due to the fact that in contrast to the latter, it is not necessary to compute the intercept parameter for the signed-rank detector proposed in this correspondence. We analyze the behavior of the blind version of this detector and show that it outperforms the blind minimax detector. We also show that this detector has a bounded influence function and hence it is robust.     

Pareto-Efficient and Goal-Driven Power Control in Wireless Networks: A Game-Theoretic Approach With a Novel Pricing Scheme

A Pareto-efficient, goal-driven, and distributed power control scheme for wireless networks is presented. We use a noncooperative game-theoretic approach to propose a novel pricing scheme that is linearly proportional to the signal-to-interference ratio (SIR) and analytically show that with a proper choice of prices (proportionality constants), the outcome of the noncooperative power control game is a unique and Pareto-efficient Nash equilibrium (NE). This can be utilized for constrained-power control to satisfy specific goals (such as fairness, aggregate throughput optimization, or trading off between these two goals). For each one of the above goals, the dynamic price for each user is also analytically obtained. In a centralized (base station) price setting, users should inform the base station of their path gains and their maximum transmit-powers. In a distributed price setting, for each goal, an algorithm for users to update their transmit-powers is also presented that converges to a unique fixed-point in which the corresponding goal is satisfied. Simulation results confirm our analytical developments.     

Nonparametric multiuser detection in non-Gaussian channels

Existing multiuser detection techniques in wireless systems are based on the assumption that some information on the parameters of the probability density function (pdf) of ambient noise is available. Such information may not be available in all cases, particularly for non-Gaussian and impulsive noises, or may change depending on circumstances. In this paper, we present a technique for multiuser detection that does not require any a priori knowledge about the noise parameters. This method is based on using pseudo norms for linear nonparametric regression. Analytical and simulation results show that the proposed method offers an improved, or at least comparable, performance over existing robust techniques in the absence of any information on the nature of noise in the environment. The increased computational complexity is marginal compared to existing parametric detectors. In addition, the proposed nonparametric detector is portable in the sense that it does not need to be tuned for different noise models without any considerable degradation of performance. We also show that in non-Gaussian noise, the performance of blind adaptive nonparametric multiuser detectors is better than that of robust multiuser detectors.     

Iterative cancellation of clipping noise in multilevel quadrature amplitude modulation multi-carrier CDMA system

An iterative reconstruction method that was proposed for clipping noise cancellation in orthogonal frequency domain multiplexing (OFDM) systems is applied to multilevel quadrature amplitude modulation-based multi-carrier (MC) code division multiple access signals in downlink. The iterative method uses a known distortion function, maybe a nonlinear one such as a successive clipping and filtering process, in the iterations to give an approximation of inverse of the distortion process and then the iterative method removes distortion under a convergence condition. The authors show that MC signal with properly chosen clipping threshold satisfies convergence conditions of the iterative method. In contrast to some of the other reconstruction-based techniques, this method requires no extra bandwidth and side information and it can be implemented with reasonable complexity. Furthermore, the authors show that the proposed iterative scheme can be enhanced by using an extra frequency bandwidth. Exploiting extra bandwidth improves the performance of the reconstruction-based methods in case of using successive clipping and filtering. Simulation results will be used to demonstrate achievable bit-error-rate improvement by the proposed enhanced iterative scheme.