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This paper presents a fast online support vector machine (FOSVM) algorithm for variable-step CDMA power control. The FOSVM algorithm distinguishes new added samples and constructs current training sample set using K.K.T. condition in order to reduce the size of training samples. As a result, the training speed is effectively increased. We classify the received signals into two classes with FOSVM algorithm, then according to the output label of FOSVM and the distance from the data points to the SIR decision boundary, variable-step power control command is determined. Simulation results illustrate that the algorithm has a fast training speed and less support vectors. Its convergence performance is better than the fixed-step power control algorithm. [ABSTRACT FROM AUTHOR]

We present a simulation-based algorithm to compute the average reward of singulary perturbed Markov Reward Processes (SPMRPs) with large scale state spaces, which depend on some sets of parameters. Compared with the original algorithm applied on these problems of general Markov Reward Processes (MRPs), our algorithm aims to obtain a faster pace in singularly perturbed cases. This algorithm relies on the special structure of singularly perturbed Markov processes, evolves along a single sample path, and hence can be applied on-line. [ABSTRACT FROM AUTHOR]