Loss ratio of the EDF scheduling policy with early discarding technique

This paper considers a firm real-time M/M/1 system, where jobs have stochastic deadlines till the end of service. A method for approximately specifying the loss ratio of the earliest-deadline-first scheduling policy along with exit control through the early discarding technique is presented. This approximation uses the arrival rate and the mean relative deadline, normalized with respect to the mean service time, for exponential and uniform distributions of relative deadlines. Simulations show that the maximum approximation error is less than 4% and 2% for the two distributions, respectively, for a wide range of arrival rates and mean relative deadlines. (C) 2013 Elsevier B.V. All rights reserved.

The dynamics between forward citations and price of singleton patents

In recent years, business practitioners are seen valuing patents on the basis of the market price that the patent can attract. Researchers have also looked into various patent latent variables and firm variables that influence the price of a patent. Forward citations of a patent are shown to play a role in determining price. Using patent auction price data (of Ocean Tomo now ICAP patent brokerage), we delve deeper into of the role of forward citations. The successfully sold 167 singleton patents form the sample of our study. We found that, it is mainly the right tail of the citation distribution that explains the high prices of the patents falling on the right tail of the price distribution. There is consistency in the literature on the positive correlation between patent prices and forward citations. In this paper, we go deeper to understand this linear relationship through case studies. Case studies of patents with high and low citations are described in this paper to understand why some patents attracted high prices. We look into the role of additional patent latent variables like age, technology discipline, class and breadth of the patent in influencing citations that a patent receives.

Analysis of an queue operated under the FCFS policy with exact admission control

In this article, we present an exact theoretical analysis of an system, with arbitrary distribution of relative deadline for the end of service, operated under the first come first served scheduling policy with exact admission control. We provide an explicit solution to the functional equation that must be satisfied by the workload distribution, when the system reaches steady state. We use this solution to derive explicit expressions for the loss ratio and the sojourn time distribution. Finally, we compare this loss ratio with that of a similar system operating without admission control, in the cases of some common distributions of the relative deadline.

SEP-Optimal Transmit Power Policy for Peak Power and Interference Outage Probability Constrained Underlay Cognitive Radios

In underlay cognitive radio (CR), a secondary user (SU) can transmit concurrently with a primary user (PU) provided that it does not cause excessive interference at the primary receiver (PRx). The interference constraint fundamentally changes how the SU transmits, and makes link adaptation in underlay CR systems different from that in conventional wireless systems. In this paper, we develop a novel, symbol error probability (SEP)-optimal transmit power adaptation policy for an underlay CR system that is subject to two practically motivated constraints, namely, a peak transmit power constraint and an interference outage probability constraint. For the optimal policy, we derive its SEP and a tight upper bound for MPSK and MQAM constellations when the links from the secondary transmitter (STx) to its receiver and to the PRx follow the versatile Nakagami-m fading model. We also characterize the impact of imperfectly estimating the STx-PRx link on the SEP and the interference. Extensive simulation results are presented to validate the analysis and evaluate the impact of the constraints, fading parameters, and imperfect estimates.

A simulation-based algorithm for optimal pricing policy under demand uncertainty

We propose a simulation-based algorithm for computing the optimal pricing policy for a product under uncertain demand dynamics. We consider a parameterized stochastic differential equation (SDE) model for the uncertain demand dynamics of the product over the planning horizon. In particular, we consider a dynamic model that is an extension of the Bass model. The performance of our algorithm is compared to that of a myopic pricing policy and is shown to give better results. Two significant advantages with our algorithm are as follows: (a) it does not require information on the system model parameters if the SDE system state is known via either a simulation device or real data, and (b) as it works efficiently even for high-dimensional parameters, it uses the efficient smoothed functional gradient estimator.

Efectos de la política monetaria en un modelo de precios rígidos en distintas monedas

Resumen: En un modelo dinámico, de dos países y con precios rígidos, este trabajo analiza la transmisión de la política monetaria cuando las empresas fijan sus precios en distintas monedas. Siguiendo el modelo de Betts y Devereux (2000) suponemos que las empresas pueden fijar un único precio para el mercado local y extranjero en moneda del país al cual exportan. Algunas empresas segmentan el mercado por país y otras fijan un único precio en su propia moneda o en la del país vecino. Los precios rígidos en moneda del país vecino aumentan la variabilidad del tipo de cambio y reducen los efectos positivos que la política monetaria tiene sobre el consumo y la tasa de interés real, respecto a una situación donde las empresas sólo segmentan el mercado o fijan un único precio en su propia moneda. En ausencia de segmentación de mercado, a mayor número de empresas que fijen su precio en moneda del país vecino, mayor es el efecto positivo que un shock monetario en el país extranjero tiene sobre su bienestar y el del otro, pero es menor en ambos cuando se produce en el país local.