Dynamic frequency assignment for mobile users in multibeam satellite constellations

Els nivells de flexibilitat i escalabilitat mai vistos de la propera generació de sistemes de comunicació per satèl·lit exigeixen nous algorismes de gestió de recursos que s'adaptin a contextos dinàmics. El futur entorn dels serveis de comunicació per satèl·lit estarà definit per un nombre més gran d'usuaris, una gran part dels quals correspondrà a usuaris mòbils com avions o vaixells. El repte addicional que introdueixen aquests usuaris és abordar la incertesa espai-temporal que es presenta en forma de retards, canvis en la seva trajectòria, o tots dos. Atès que els usuaris mòbils constituiran un segment important del mercat, els operadors de satèl·lits prioritzen l'aprofitament dels avançats sistemes digitals per desenvolupar estratègies flexibles d'assignació de recursos que siguin robustes davant de les bases d'usuaris dinàmiques. Un dels problemes clau en aquest context és com gestionar l'espectre de freqüències de manera eficient. Mentre que nombroses solucions aborden escenaris d'assignació de dinàmica freqüències, el nivell addicional de complexitat que presenten els usuaris mòbils no ha estat prou estudiat, i no és clar si els nous algorismes d'assignació de freqüències poden abordar la incertesa espai-temporal. Concretament, sostenim que els canvis inesperats en la posició dels usuaris introdueixen noves restriccions en l'assignació de freqüències que els algoritmes la literatura podrien no ser capaços de complir, especialment si les decisions s'han de prendre en temps real i a escala. Per solucionar aquesta limitació, proposem un algorisme de gestió dinàmica de freqüències basat en programació lineal entera que assigna recursos a escenaris amb usuaris tant fixos com mòbils, tenint en compte la incertesa espai-temporal d'aquests últims. El nostre mètode inclou tant la planificació a llarg termini com l'operació en temps real, una sinergia que no ha estat prou explorada per a les comunicacions per satèl·lit i que és crítica quan s'opera sota incertesa. PLos niveles de flexibilidad y escalabilidad nunca vistos de la próxima generación de sistemas de comunicación por satélite exigen nuevos algoritmos de gestión de recursos que se adapten a contextos dinámicos. El futuro entorno de los servicios de comunicación por satélite estará definido por un mayor número de usuarios, una gran parte de los cuales corresponderá a usuarios móviles como aviones o barcos. El reto adicional que introducen estos usuarios es abordar la incertidumbre espacio-temporal que se presenta en forma de retrasos, cambios en su trayectoria, o ambos. Dado que los usuarios móviles constituirán un segmento importante del mercado, los operadores de satélites dan prioridad al aprovechamiento de los avanzadas sistemas digitales para desarrollar estrategias flexibles de asignación de recursos que sean robustas frente a las bases de usuarios dinámicas. Uno de los problemas clave en este contexto es cómo gestionar el espectro de frecuencias de forma eficiente. Mientras que numerosas soluciones abordan escenarios de asignación dinámica de frecuencias, el nivel adicional de complejidad que presentan los usuarios móviles no ha sido suficientemente estudiado, y no está claro si los nuevos algoritmos de asignación de frecuencias pueden abordar la incertidumbre espacio-temporal. En concreto, sostenemos que los cambios inesperados en la posición de los usuarios introducen nuevas restricciones en la asignación de frecuencias que los algoritmos la literatura podrían no ser capaces de cumplir, especialmente si las decisiones deben tomarse en tiempo real y a escala. Para solventar esta limitación, proponemos un algoritmo de gestión dinámica de frecuencias basado en la programación lineal entera que asigna recursos en escenarios con usuarios tanto fijos como móviles, teniendo en cuenta la incertidumbre espacio-temporal de estos últimos. Nuestro método incluye tanto la planificación a largo plazo como la operación en tiempo real, una sinergia que no ha sido suficientThe unprecedented levels of flexibility and scalability of the next generation of communication satellite systems call for new resource management algorithms that adapt to dynamic environments. The upcoming landscape of satellite communication services will be defined by an increased number of unique users, a large portion of which will correspond to mobile users such as planes or ships. The additional challenge introduced by these users is addressing the spatiotemporal uncertainty that comes in the form of delays, changes in their trajectory, or both. Given that mobile users will constitute an important segment of the market, satellite operators prioritize leveraging modern digital payloads to develop flexible resource allocation strategies that are robust against dynamic user bases. One of the key problems in this context is how to manage the frequency spectrum efficiently. While numerous solutions address dynamic frequency assignment scenarios, the additional layer of complexity presented by mobile users has not been sufficiently studied, and it is unclear whether novel frequency assignment algorithms can address spatiotemporal uncertainty. Specifically, we argue that unexpected changes in the position of users introduce new restrictions into the frequency assignment, which previous algorithms in the literature might not be able to meet, especially if decisions need to be made in real-time and at scale. To address this gap, we propose a dynamic frequency management algorithm based on integer linear programming that assigns resources in scenarios with both fixed and mobile users, accounting for the spatiotemporal uncertainty of the latter. Our method includes both long-term planning and real-time operation, a synergy that has not been sufficiently explored for satellite communications and proves to be critical when operating under uncertainty. To fulfill the problem’s scope, we propose different strategies that extend a state-of-the-art frequency management algOutgoin

Traveling Salesman Problem

This book is a collection of current research in the application of evolutionary algorithms and other optimal algorithms to solving the TSP problem. It brings together researchers with applications in Artificial Immune Systems, Genetic Algorithms, Neural Networks and Differential Evolution Algorithm. Hybrid systems, like Fuzzy Maps, Chaotic Maps and Parallelized TSP are also presented. Most importantly, this book presents both theoretical as well as practical applications of TSP, which will be a vital tool for researchers and graduate entry students in the field of applied Mathematics, Computing Science and Engineering

Technology Directions for the 21st Century

The Office of Space Communications (OSC) is tasked by NASA to conduct a planning process to meet NASA's science mission and other communications and data processing requirements. A set of technology trend studies was undertaken by Science Applications International Corporation (SAIC) for OSC to identify quantitative data that can be used to predict performance of electronic equipment in the future to assist in the planning process. Only commercially available, off-the-shelf technology was included. For each technology area considered, the current state of the technology is discussed, future applications that could benefit from use of the technology are identified, and likely future developments of the technology are described. The impact of each technology area on NASA operations is presented together with a discussion of the feasibility and risk associated with its development. An approximate timeline is given for the next 15 to 25 years to indicate the anticipated evolution of capabilities within each of the technology areas considered. This volume contains four chapters: one each on technology trends for database systems, computer software, neural and fuzzy systems, and artificial intelligence. The principal study results are summarized at the beginning of each chapter

The 1990 Goddard Conference on Space Applications of Artificial Intelligence

The papers presented at the 1990 Goddard Conference on Space Applications of Artificial Intelligence are given. The purpose of this annual conference is to provide a forum in which current research and development directed at space applications of artificial intelligence can be presented and discussed. The proceedings fall into the following areas: Planning and Scheduling, Fault Monitoring/Diagnosis, Image Processing and Machine Vision, Robotics/Intelligent Control, Development Methodologies, Information Management, and Knowledge Acquisition

A Decade of Neural Networks: Practical Applications and Prospects

The Jet Propulsion Laboratory Neural Network Workshop, sponsored by NASA and DOD, brings together sponsoring agencies, active researchers, and the user community to formulate a vision for the next decade of neural network research and application prospects. While the speed and computing power of microprocessors continue to grow at an ever-increasing pace, the demand to intelligently and adaptively deal with the complex, fuzzy, and often ill-defined world around us remains to a large extent unaddressed. Powerful, highly parallel computing paradigms such as neural networks promise to have a major impact in addressing these needs. Papers in the workshop proceedings highlight benefits of neural networks in real-world applications compared to conventional computing techniques. Topics include fault diagnosis, pattern recognition, and multiparameter optimization

Joint University Program for Air Transportation Research, 1991-1992

This report summarizes the research conducted during the academic year 1991-1992 under the FAA/NASA sponsored Joint University Program for Air Transportation Research. The year end review was held at Ohio University, Athens, Ohio, June 18-19, 1992. The Joint University Program is a coordinated set of three grants sponsored by the Federal Aviation Administration and NASA Langley Research Center, one each with the Massachusetts Institute of Technology (NGL-22-009-640), Ohio University (NGR-36-009-017), and Princeton University (NGL-31-001-252). Completed works, status reports, and annotated bibliographies are presented for research topics, which include navigation, guidance and control theory and practice, intelligent flight control, flight dynamics, human factors, and air traffic control processes. An overview of the year's activities for each university is also presented

Energy management engineering : a predictive energy management system incorporating an adaptive neural network for the direct heating of domestic and industrial fluid mediums.

The objective of this research project is to improve the control and provide a more cost-efficient operation in the direct heating of stored domestic or industrial fluid mediums; such to be achieved by means of an intelligent automated energy management system. For the residential customer this system concept applies to the hot water supply as stored in the familiar hot water cylinder; for the industrial or commercial customer the scope is considerably greater with larger quantities and varieties of fluid mediums. Both areas can obtain significant financial savings with improved energy management. Both consumers and power supply and distribution companies will benefit with increased utilisation of cheaper 'off-peak' electricity; reducing costs and spreading the system load demand. The project has focussed on domestic energy management with a definite view to the wider field of industrial applications. Domestic energy control methodology and equipment has not significantly altered for decades. However, computer hardware and software has since then flourished to an unprecedented proportion and has become relatively cheap and versatile; these factors pave the way for the application of computer technology in this area of great potential. The technology allows the implementation of a 'hot water energy management system', which makes a forecast of the hot water demand for the next 24 hours and proceeds to provide this demand in the most efficient manner possible. In the (near) future, the system, known as FEMS for Fluid Energy Management System, is able to take advantage and in fact will promote the use of a retail 'dynamic spot price tariff’. FEMS is a combination of hardware and software developed to replace the existing cylinder thermostat, take care of the necessary data-acquisition and control the cylinder's total energy instead of it's (single point) temperature. This provides, besides heating cost reduction, a greater accuracy, a degree of flexibility, improved feedback, legionella inhibition, and a diagnostic capability. To the domestic consumer the latter three items are of greatest relevance. The crux of the system lies in its predictive ability. Having explored the more conventional alternatives, a suitable solution was found in the utilisation of the Elman recurrent neural networks, which focus on the temporal characteristics of the hot water demand time series and are able to adapt to changing environments, coping with the presence of any non-linearity and noise in the data. Prior to developing FEMS a study was made of the basic fluid behaviour in medium and high pressure domestic hot water cylinders, an area not well-covered to date and of interest to engineers and manufacturers alike. For this step data acquisition equipment and software was purposely created. The control software plus equipment were combined into a fully automated test system with minimal operator input, allowing a large amount of data to be gathered over a period measured in months. A similar system was subsequently used to collect actual hot water demand data from a residential family, and in fact forms the basis for FEMS. Finally an enhanced version of FEMS is discussed and it is shown how the system is able to output multiple prediction and utilise varying tariff rates

The 1989 Goddard Conference on Space Applications of Artificial Intelligence

The following topics are addressed: mission operations support; planning and scheduling; fault isolation/diagnosis; image processing and machine vision; data management; and modeling and simulation