An insightful, comprehensive, and up-to-date treatment of linear, nonlinear, and
discrete/combinatorial network optimization problems, their applications, and their analytical …

In order to meet the growing needs for wireless communication in dynamic and diverse
circumstances, Cognitive Radio Networks (CRNs) have evolved as a transformational …

Geometric Programming (GP) is a class of nonlinear optimization with many useful
theoretical and computational properties. Over the last few years, GP has been used to solve …

Information theory has not yet had a direct impact on networking, although there are
similarities in concepts and methodologies that have consistently attracted the attention of …

Transmitter power control can be used to concurrently achieve several key objectives in
wireless networking, including minimizing power consumption and prolonging the battery …

A decentralized control algorithm is sought that maximizes the stability region of the infinite-
user slotted multipacket channel and is easily implementable. To this end, the perfect state …

Neural Networks: Computational Models and Applications covers a wealth of important
theoretical and practical issues in neural networks, including the learning algorithms of feed …

Markov decision processes (MDPs), also called stochastic dynamic programming, were first
studied in the 1960s. MDPs can be used to model and solve dynamic decision-making …

We consider the downlink rate control problem in a wireless channel. A dynamic
programming optimization method is introduced to obtain the optimal bit-rate/delay control …

We present in this chapter a survey on applications of MDPs to communication networks. We
survey both the different application areas in communication networks as well as the …