Institute for Systems Research Technical Reports

The current plan in the modernization of the A/G communications is to migrate from analog voice only system to integrated digital voice and data system. The next generation satellite systems can be an alternative to the terrestrial A/G systems by their low propagation and transmission delays, global coverage, high capacity, and free flight suitable characteristics. In this paper, we give an overview of the current and the future ATC architectures, describe the systems and the communications issues in these systems, and develop a framework in which LEO/MEO next generation satellite systems can be integrated to the future ATC systems.

In this paper, we address the rate control problem for multirate multicast sessions,with the objective of maximizing the total receiver utility.This aggregate utility maximization problem not only takes into account the heterogeneityin user requirements, but also provides a unified framework for diverse fairness objectives.

We propose an algorithm for this problem and show, through analysis andsimulation, that it converges to the optimal rates.

In spite of the non-separability of the problem,the solution that we develop is completely decentralized, scalableand does not require the network to know the receiver utilities.Moreover, the algorithm requires very simple computations both forthe user and the network, and also has very low overhead of network congestionfeedback.

We propose a simple algorithm for achieving the optimal rates for this problem. The algorithm can be implemented in a distributed way and does not require the network to know the user utility functions.

In our algorithm, the network communicates to the user the number of congested links on the user's path, and the user (end-host) adjusts its rate accordingly, taking into account its utility function and the network congestion feedback.

We show through analysis and experimentation that our algorithm converges to the optimum rates.

In this paper, we address the problem of achieving rates that maximize the total receiver utility for multirate multicast sessions. This problem not only takes into account the heterogeneity in user requirements, but also provides a unified framework for diverse fairness objectives. We propose two algorithms and prove that they converge to the optimal rates for this problem.

The algorithms are distributed and scalable, and do not require the network to know the receiver utilities. We discuss how these algorithms can be implemented in a real network, and also demonstrate their convergence through simulation experiments.

This is a mixture of the traditional prefix-routed scenario fo the fixed Internet, and the classical edge mobility scenario that is today supported by cellularnetworks, primarily as part of the cellular technology elements (GSM, GPRS, etc.).

We outline a general architecture for the support of such edge mobility, and present an approach to FMC routing that fits within this architecture. We then present initial simulation resultsillustrating the potential scalability and routing efficiency of this approach.

Stochastic optimization is a direct application of the above root finding problem if the SA is driven by a gradient estimate of steady state performance. We study the convergence properties of an SA driven by agradient estimator which observes an increasing number of samples from the Markov chain at each step of the SA's recursion. To show almost sure convergence to the optimizer, a framework of verifiable conditions is introduced which builds on the general SA conditions proposed for the root finding problem.

We also consider a difficulty sometimes encountered in applicationswhen selecting the set used in the projection operator of the SA algorithm.Suppose there exists a well-behaved positive recurrent region of the state process parameter space where the convergence conditions are satisfied; this being the ideal set to project on. Unfortunately, the boundaries of this projection set are not known a priori when implementing the SA. Therefore, we consider the convergence properties when the projection set is chosen to include regions outside the well-behaved region. Specifically, we consider an SA applied to an M/M/1 which adjusts the service rate parameter when the projection set includes parameters that cause the queue to be transient.

Finally, we consider an alternative SA where the recursion is driven by a sample average of observations. We develop conditions implying convergence for this algorithm which are based on a uniform large deviation upper bound and we present specialized conditions implyingthis property for finite state Markov chains.

The UT must operate both in full- and half-duplex mode, the latter being desirable when power limitations are imposed. We propose a scheme that achieves this goal and guarantees efficient diversity provision. Constant delay contours on the earth's surface are defined. The problem of reliable time delay acquisition is addressed, in case synchronization is lost. The SBS solves that either by using the known estimate of UT position or by requesting a measurement report by the UT.

The problem of channel allocation appears in cellular networks of every kind. Calls arising in the cell overlap area have access to channels of more than one base station and may choose which base station they will use to establish connection. In that case the problems of base station and channel assignment arise jointly.

We address the problem in a linear cellular network and aim at the minimumnumber of utilized channels. We present two algorithms: The first one expands Load Balancing in clique populations and is Sequential Clique Load Balancing (SCLB). The second one is named Clique Load Balancing with Inverse Water-Filling (CLB-IWF). In a dynamic environment, we unify SCLB and CLB-IWF into CLB-DA, which comprises Dynamic Allocation. CLB-DA is compared with Least Loaded Routing (LLR) policy and with Random Routing policy. We finally deduce that at light loads CLB-DA outperforms LLR, attaining smaller blocking probability, whereas at heavier loads all three policies converge.