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Abstract:We consider the problem of service rate control of a single-server queueing system with a finite-state Markov-modulated Poisson arrival process. We show that the optimal service rate is nondecreasing in the number of customers in the system; higher congestion levels warrant higher service rates. On the contrary, however, we show that the optimal service rate is not necessarily monotone in the current arrival rate. If the modulating process satisfies a stochastic monotonicity property, the monotonicity is recovered. We examine several heuristics and show where heuristics are reasonable substitutes for the optimal control. None of the heuristics perform well in all the regimes and the fluctuation rate of the modulating process plays an important role in deciding the right heuristic. Second, we discuss when the Markov-modulated Poisson process with service rate control can act as a heuristic itself to approximate the control of a system with a periodic nonhomogeneous Poisson arrival process. Not only is the current model of interest in the control of Internet or mobile networks with bursty traffic, but it is also useful in providing a tractable alternative for the control of service centers with nonstationary arrival rates.

Non-orthogonal multiple access (NOMA) has been considered as one of the key enabling techniques for next-generation wireless networks and the overall performance of these systems largely depends on the power allocation (PA). In this Letter, the authors address the PA problem in a downlink cellular NOMA network. They propose an auction-based mechanism in which the users compete for the transmit power being sold by the base station. Each user places his bid iteratively in order to maximise his own utility. Further, the authors prove the existence of an unique Nash equilibrium theoretically. Simulation results demonstrate the effectiveness of the proposed scheme in terms of the average sum rate of users as compared to an existing algorithm.

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This paper attempts to estimate the probability distribution of music signals. A number of music signals belonging to different genres of music have been analyzed. Four well known speech distributions viz. Gaussian, Generalized Gamma, Laplacian and Cauchy have been tested as hypotheses. The distribution estimation has been carried out in time and Discrete-Cosine-Transform (DCT) domains. It was observed that skewed Laplacian distribution describes the music samples most accurately with the peakedness of the distribution being correlated with the genre of music. Although Cauchy distribution along with Laplacian has been a good fit for most of the data, it is analytically shown in this work that Laplacian distribution is a better choice for modeling music signals.