Joint Channel and Power Allocation in Tactical Cognitive Networks: Enhanced Trial and Error

National audienceIn tactical networks the presence of a central controller (e.g., a base station) is made impractical by the unpredictability of the nodes' positions and by the fact that its presence can be exploited by hostile entities. As a consequence, self-configuring networks are sought for military and emergency communication networks. In such networks, the transmission parameters, most notably the transmission channel and the power level, are set by the devices following specific behavioural rules. In this context, an algorithm for self-configuring wireless networks is presented, analysed and enhanced to meet the specific needs of tactical networks. Such an algorithm, based on the concept of trial and error, is tested under static and mobile situations, and different metrics are considered to show its performance. In particular, the stability and performance improvements with respect to previously proposed versions of the algorithm are detailed

Channel and power allocation algorithms for ad hoc clustered networks

978-1-4673-1422-0International audienceIn the context of mobile clustered ad hoc networks, this paper proposes and studies a self-configuring algorithm which is able to jointly set the channel frequency and power level of the transmitting nodes, by exploiting one bit of feedback per receiver. This algorithm is based upon a learning algorithm, namely trial and error, that is cast into a game theoretical framework in order to study its theoretical performance. We consider two different feedback solutions, one based on the SINR level estimation, and one based on the outcome of a CRC check. We analytically prove that this algorithm selects a suitable configuration for the network, and analyse its performance through numerical simulations under various scenarios

Achieving Pareto Optimal Equilibria in Energy Efficient Clustered Ad Hoc Networks

International audienceIn this paper, a decentralized iterative algorithm able to achieve a Pareto optimal working point in a clustered ad hoc network is analysed. Here, radio devices are assumed to operate above a minimal signal to interference plus noise ratio (SINR) threshold while minimizing the global power consumption. A distributed algorithm, namely the optimal dynamic learning (ODL), is presented and shown to be able to dynamically steer the network to an efficient working point, by exploiting only minimal amount of information. This algorithm aims at implementing a Pareto optimal solution for a large proportion of the time, with high probability. Conversely, existing solutions aim at achieving individually optimal solutions (Nash equilibria), which might be globally inefficient. The gain is shown to be larger when the amount of available radio resource is scarce. Sufficient analytical conditions for ODL to converge to the desired working point are provided, moreover through numerical simulations the ability of the algorithm to configure an interference limited network is shown. The performance of ODL and those of a Nash equilibrium reaching algorithm are numerically compared, and their performance as a function of available resources studied

Distributed Power Allocation with SINR Constraints Using Trial and Error Learning

978-1-4673-0436-8International audienceIn this paper, we address the problem of global transmit power minimization in a self-configuring network where radio devices are subject to operate at a minimum signal to interference plus noise ratio (SINR) level. We model the network as a parallel Gaussian interference channel and we introduce a fully decentralized algorithm (based on trial and error) able to statistically achieve a configuration where the performance demands are met. Contrary to existing solutions, our algorithm requires only local information and can learn stable and efficient working points by using only one bit feedback. We model the network under two different game theoretical frameworks: normal form and satisfaction form. We show that the converging points correspond to equilibrium points, namely Nash and satisfaction equilibrium. Similarly, we provide sufficient conditions for the algorithm to converge in both formulations. Moreover, we provide analytical results to estimate the algorithm's performance, as a function of the network parameters. Finally, numerical results are provided to validate our theoretical conclusions

Self-Organization in Decentralized Networks: A Trial and Error Learning Approach

International audienceIn this paper, the problem of channel selection and power control is jointly analyzed in the context of multiple-channel clustered ad-hoc networks, i.e., decentralized networks in which radio devices are arranged into groups (clusters) and each cluster is managed by a central controller (CC). This problem is modeled by game in normal form in which the corresponding utility functions are designed for making some of the Nash equilibria (NE) to coincide with the solutions to a global network optimization problem. In order to ensure that the network operates in the equilibria that are globally optimal, a learning algorithm based on the paradigm of trial and error learning is proposed. These results are presented in the most general form and therefore, they can also be seen as a framework for designing both games and learning algorithms with which decentralized networks can operate at global optimal points using only their available local knowledge. The pertinence of the game design and the learning algorithm are highlighted using specific scenarios in decentralized clustered ad hoc networks. Numerical results confirm the relevance of using appropriate utility functions and trial and error learning for enhancing the performance of decentralized networks

Multiwavelength Observations of 1ES 1959+650, One Year After the Strong Outburst of 2002

In April-May 2003, the blazar 1ES 1959+650 showed an increased level of X-ray activity. This prompted a multiwavelength observation campaign with the Whipple 10 m gamma-ray telescope, the Rossi X-ray Timing Explorer, the Bordeaux Optical Observatory, and the University of Michigan Radio Astrophysical Observatory. We present the multiwavelength data taken from May 2, 2003 to June 7, 2003 and compare the source characteristics with those measured during observations taken during the years 2000 and 2002. The X-ray observations gave a data set with high signal-to-noise light curves and energy spectra; however, the gamma-ray observations did not reveal a major TeV gamma-ray flare. Furthermore, we find that the radio and optical fluxes do not show statistically significant deviations from those measured during the 2002 flaring periods. While the X-ray flux and X-ray photon index appear correlated during subsequent observations, the apparent correlation evolved significantly between the years 2000, 2002, and 2003. We discuss the implications of this finding for the mechanism that causes the flaring activity.Comment: 17 pages, 6 figures, 2 table

Search for TeV Gamma-Rays from Shell-Type Supernova Remnants

If cosmic rays with energies <100 TeV originate in the galaxy and are accelerated in shock waves in shell-type supernova remnants (SNRs), gamma-rays will be produced as the result of proton and electron interactions with the local interstellar medium, and by inverse Compton emission from electrons scattering soft photon fields. We report on observations of two supernova remnants with the Whipple Observatory's 10 m gamma-ray telescope. No significant detections have been made and upper limits on the >500 GeV flux are reported. Non-thermal X-ray emission detected from one of these remnants (Cassiopeia A) has been interpreted as synchrotron emission from electrons in the ambient magnetic fields. Gamma-ray emission detected from the Monoceros/Rosette Nebula region has been interpreted as evidence of cosmic-ray acceleration. We interpret our results in the context of these observations.Comment: 4 pages, 2 figures, to appear in the proceedings of 26th International Cosmic Ray Conference (Salt Lake City, 1999

Balancing Selection at the Tomato RCR3 Guardee Gene Family Maintains Variation in Strength of Pathogen Defense

Coevolution between hosts and pathogens is thought to occur between interacting molecules of both species. This results in the maintenance of genetic diversity at pathogen antigens (or so-called effectors) and host resistance genes such as the major histocompatibility complex (MHC) in mammals or resistance (R) genes in plants. In plant-pathogen interactions, the current paradigm posits that a specific defense response is activated upon recognition of pathogen effectors via interaction with their corresponding R proteins. According to the''Guard-Hypothesis,'' R proteins (the ``guards'') can sense modification of target molecules in the host (the ``guardees'') by pathogen effectors and subsequently trigger the defense response. Multiple studies have reported high genetic diversity at R genes maintained by balancing selection. In contrast, little is known about the evolutionary mechanisms shaping the guardee, which may be subject to contrasting evolutionary forces. Here we show that the evolution of the guardee RCR3 is characterized by gene duplication, frequent gene conversion, and balancing selection in the wild tomato species Solanum peruvianum. Investigating the functional characteristics of 54 natural variants through in vitro and in planta assays, we detected differences in recognition of the pathogen effector through interaction with the guardee, as well as substantial variation in the strength of the defense response. This variation is maintained by balancing selection at each copy of the RCR3 gene. Our analyses pinpoint three amino acid polymorphisms with key functional consequences for the coevolution between the guardee (RCR3) and its guard (Cf-2). We conclude that, in addition to coevolution at the ``guardee-effector'' interface for pathogen recognition, natural selection acts on the ``guard-guardee'' interface. Guardee evolution may be governed by a counterbalance between improved activation in the presence and prevention of auto-immune responses in the absence of the corresponding pathogen