Panayota Papantoni-Kazakos

A simple window random access algorithm with advantageous properties

A simple full-feedback-sensing window random access algorithm is proposed and analyzed. The throughput of the algorithm is 0.429; its delay and resistance to feedback channel errors are better than those induced by J.I. Capetanakiss (ibid., vol.IT-25, p.505-15, Sept. 1979) window algorithm. In addition, the simple operations of the algorithm allow for the analytical evaluation of the output-traffic interdeparture distribution. >

An algorithm for detecting a change in a stochastic process

The problem of detecting a change from one given stationary and ergodic stochastic process to another such process is considered. It is assumed that both stochastic processes are processes with memory and that they are mutually independent. A sequential test is proposed and analyzed. It is proved that the proposed test is asymptotically optimal in a mathematically precise sense.

A Collision Resolution Protocol for Random Access Channels with Energy Detectors

In this paper, we consider the random accessing of a single slotted channel by a large number of packet-transmitting, bursty users. We assume that feedback broadcasting is available where some different information, in addition to the information assumed by the Capetanakis, Gallager, Massey, etc., models, is included in the feedback. In particular, we assume that the existence of energy detectors permits the broadcasting of the number of collided packets within each collision slot, whenever this number is below a certain limit. We first consider this limit to be infinity, and then a finite small number. For the model considered, we propose and analyze a collision resolution protocol (CRAI), whose implementation is simple. For Poisson input traffic and infinite number of energy detectors, we found that the CRAI is stable for input rates below 0.53237. For finite number of energy detectors, we propose a modified version of the CRAI (MCRAI). We found that the MCRAI reaches the throughput 0.53237, through the utilization of only about eight energy detectors. These protocols, like the ones introduced by Capetanakis, Gallager, Massey, etc., have good delay properties.

On the Relation Between the Finite and the Infinite Population Models for a Class of RAA's

We examine the relation between the finite and the infinite population models for a class of random access algorithms. The algorithms in the class are a combination of random access and reservation techniques, they are synchronous, and they are studied under the condition that each of the users can monitor the channel feedback continuously (full feedback sensing). For any finite number of independent and identical users in the system, and any i.i.d. arrival process per user, the algorithms are stable, provided that the total input rate is less than one. However, as the population size increases, the stability of an algorithm in the class is determined by its throughput in the presence of the infinite population model for all practical purposes.

Distributed binary hypothesis testing with feedback

The problem of binary hypothesis testing is revisited in the context of distributed detection with feedback. Two basic distributed structures with decision feedback are considered. The first structure is the fusion center network, with decision feedback connections from the fusion center element to each one of the subordinate decisionmakers. The second structure consists of a set of detectors that are fully interconnected via decision feedback. Both structures are optimized in the Neyman-Pearson sense by optimizing each decision-maker individually. Then, the time evolution of the power of the tests is derived. Definite conclusions regarding the gain induced by the feedback process and direct comparisons between the two structures and the optimal centralized scheme are obtained through asymptotic studies (that is, assuming the presence of asymptotically many local detectors). The behavior of these structures is also examined in the presence of variations in the statistical description of the hypotheses. Specific robust designs are proposed and the benefits from robust operations are established. Numerical results provide additional support to the theoretical arguments. >

A full sensing window Random-Access algorithm for messages with strict delay constraints

We consider the Channel Multiple-Access problem for messages with strict delay constraints. The constraints are represented by an upper bound on the transmission delays. For this problem, and for binary collision-noncollision feedback per slot, we present a simple full sensing window Random-Access algorithm. We analyze the algorithm and we compute the fraction of maintained traffic and the expected delay for the successfully transmitted packet, for various input Poisson intensities and various values of the bound on the transmission delays.

A 0.487 throughput limited sensing algorithm

We consider Poisson packet traffic accessing a single-slotted channel. We assume the existence of a ternary feedback per channel slot. We also adopt the limited feedback sensing model where each user senses the feedback only while he has a packet to transmit. For this model we develop a collision resolution algorithm with last come-first served characteristics. The algorithm attains the same throughput as Gallagers algorithm without the latters full feedback sensing requirement. In addition, it is easy to implement, requires reasonable memory storage, induces uniformly good transmission delays, and is insensitive to feedback errors. In the presence of binary (collision versus noncollision) feedback the algorithm may attain a throughput of 0.4493 .

Multiple-access algorithms for a system with mixed traffic: high and low priority

The author considers a system where a single channel is shared by both high- and low-priority data. He assumes packet transmissions from both data categories and slotted channel. In addition, he assumes binary (collision versus noncollision) feedback per slot, and limited feedback sensing capabilities for all users in the system. He assumes that the high-priority data are generated by a well-defined finite-number user population, while he adopts the limit Poisson user model (infinitely many independent Bernoulli users) for the low-priority traffic. For this system, he proposes and analyzes a transmission algorithm which is a mixture of a deterministic tree search for the high-priority users, and a random-access algorithm for the low-priority traffic. The algorithm is stable for both traffic classes, it guarantees a strict upper bound on the delays of the high-priority packets, and induces good throughput-delay characteristics for the low-priority data. >

On-line threshold learning for Neyman-Pearson distributed detection

This paper considers the problem of Neyman-Pearson distributed detection. In distributed detection structures, a number of subordinate decision makers decide upon the active hypothesis based on their own data, and then transmit these decisions to one or more primary decision makers. Then the Neyman-Pearson performance criterion is deployed, the objective is to maximize the probability of detection (also known as power probability) induced by the primary decision makers, subject to a given false alarm constraint. In this formulation, the overall optimization problem reduces to the problem of threshold evaluation. This paper deals exactly with this issue. An on-line threshold learning algorithm is proposed that operates directly an data and requires-no explicit knowledge of the underlying probability distributions. The algorithm adapts recursively the pertinent threshold parameters in a way that minimizes the Kullback-Leibler distance between the observed and the desired output distribution. A formal convergence study is carried out and shows that, under some general conditions, the algorithm is strongly consistent; that is, the sequences of the produced threshold estimates converge to the optimal threshold values with probability 1. The rate of convergence is examined, and methods for controlling it are proposed. Simulation results are included and provide additional support to the theoretical arguments. >

Limited feedback sensing algorithms for the packet broadcast channel

A slotted packet broadcast channel with an infinite user population is considered. A limited feedback sensing algorithm is proposed and analyzed for collision versus noncollision binary feedback. The algorithm bas maximum throughput equal to 0.42 (packets/slot), has uniformly good delay characteristics within its stability region, and is robust in the presence of feedback errors. A variation of the algorithm, for ternary feedback, attains maximum throughput 0.425 and bas uniformly good delay characteristics within its stability region. In contrast, the highest throughput limited feedback sensing algorithm existing for ternary feedback attains maximum throughput 0.456 , but induces relatively high delays for Poisson intensities below 0.3 .