Cooperative Transmission and Radio Resources Management for Wireless Networks

Report for the scientific sojourn at the Multimedia Communications Laboratory, University of Texas at Dallas, USA, from September to December 2005. The cooperative transmission has been analyzed taking a broadcast relay channel which assumes a scenario with one source and multiple destinations. Moreover, in order to improve the performance in terms of mutual information, it has been considered that for each destination there is another nearby terminal (called relay) which will help to improve the performance of the destination. This scheme combines different types of channels considered in the information theory, such as the relay channel, broadcast channel and interference channel (if the relays transmit information intended only to its associated destination). In this work, the author has studied the optimal way to encode the signals for the different users, known as capacity region (i.e. related to radio resources management ), of the broadcast relay channel.

Dynamic bandwidth management as part of an integrated network management system based on distributed agents

We present a system for dynamic network resource configuration in environments with bandwidth reservation and path restoration mechanisms. Our focus is on the dynamic bandwidth management results, although the main goal of the system is the integration of the different mechanisms that manage the reserved paths (bandwidth, restoration, and spare capacity planning). The objective is to avoid conflicts between these mechanisms. The system is able to dynamically manage a logical network such as a virtual path network in ATM or a label switch path network in MPLS. This system has been designed to be modular in the sense that in can be activated or deactivated, and it can be applied only in a sub-network. The system design and implementation is based on a multi-agent system (MAS). We also included details of its architecture and implementation

Network revenue management with product-specific no-shows

Revenue management practices often include overbooking capacity to account for customerswho make reservations but do not show up. In this paper, we consider the network revenuemanagement problem with no-shows and overbooking, where the show-up probabilities are specificto each product. No-show rates differ significantly by product (for instance, each itinerary andfare combination for an airline) as sale restrictions and the demand characteristics vary byproduct. However, models that consider no-show rates by each individual product are difficultto handle as the state-space in dynamic programming formulations (or the variable space inapproximations) increases significantly. In this paper, we propose a randomized linear program tojointly make the capacity control and overbooking decisions with product-specific no-shows. Weestablish that our formulation gives an upper bound on the optimal expected total profit andour upper bound is tighter than a deterministic linear programming upper bound that appearsin the existing literature. Furthermore, we show that our upper bound is asymptotically tightin a regime where the leg capacities and the expected demand is scaled linearly with the samerate. We also describe how the randomized linear program can be used to obtain a bid price controlpolicy. Computational experiments indicate that our approach is quite fast, able to scale to industrialproblems and can provide significant improvements over standard benchmarks.

Proving the performance of a new revenue management system

Revenue management (RM) is a complicated business process that can best be described ascontrol of sales (using prices, restrictions, or capacity), usually using software as a tool to aiddecisions. RM software can play a mere informative role, supplying analysts with formatted andsummarized data who use it to make control decisions (setting a price or allocating capacity fora price point), or, play a deeper role, automating the decisions process completely, at the otherextreme. The RM models and algorithms in the academic literature by and large concentrateon the latter, completely automated, level of functionality.A firm considering using a new RM model or RM system needs to evaluate its performance.Academic papers justify the performance of their models using simulations, where customerbooking requests are simulated according to some process and model, and the revenue perfor-mance of the algorithm compared to an alternate set of algorithms. Such simulations, whilean accepted part of the academic literature, and indeed providing research insight, often lackcredibility with management. Even methodologically, they are usually awed, as the simula-tions only test \within-model" performance, and say nothing as to the appropriateness of themodel in the first place. Even simulations that test against alternate models or competition arelimited by their inherent necessity on fixing some model as the universe for their testing. Theseproblems are exacerbated with RM models that attempt to model customer purchase behav-ior or competition, as the right models for competitive actions or customer purchases remainsomewhat of a mystery, or at least with no consensus on their validity.How then to validate a model? Putting it another way, we want to show that a particularmodel or algorithm is the cause of a certain improvement to the RM process compared to theexisting process. We take care to emphasize that we want to prove the said model as the causeof performance, and to compare against a (incumbent) process rather than against an alternatemodel.In this paper we describe a \live" testing experiment that we conducted at Iberia Airlineson a set of flights. A set of competing algorithms control a set of flights during adjacentweeks, and their behavior and results are observed over a relatively long period of time (9months). In parallel, a group of control flights were managed using the traditional mix of manualand algorithmic control (incumbent system). Such \sandbox" testing, while common at manylarge internet search and e-commerce companies is relatively rare in the revenue managementarea. Sandbox testing has an undisputable model of customer behavior but the experimentaldesign and analysis of results is less clear. In this paper we describe the philosophy behind theexperiment, the organizational challenges, the design and setup of the experiment, and outlinethe analysis of the results. This paper is a complement to a (more technical) related paper thatdescribes the econometrics and statistical analysis of the results.

Modelling bird species richness with neural networks for forest landscape management in NE Spain

Para preservar la biodiversidad de los ecosistemas forestales de la Europa mediterránea en escenarios actuales y futuros de cambio global mediante una gestión forestal sostenible es necesario determinar cómo influye el medio ambiente y las propias características de los bosques sobre la biodiversidad que éstos albergan. Con este propósito, se analizó la influencia de diferentes factores ambientales y de estructura y composición del bosque sobre la riqueza de aves forestales a escala 1 × 1 km en Cataluña (NE de España). Se construyeron modelos univariantes y multivariantes de redes neuronales para respectivamente explorar la respuesta individual a las variables y obtener un modelo parsimonioso (ecológicamente interpretable) y preciso. La superficie de bosque (con una fracción de cabida cubierta superior a 5%), la fracción de cabida cubierta media, la temperatura anual y la precipitación estival medias fueron los mejores predictores de la riqueza de aves forestales. La red neuronal multivariante obtenida tuvo una buena capacidad de generalización salvo en las localidades con una mayor riqueza. Además, los bosques con diferentes grados de apertura del dosel arbóreo, más maduros y más diversos en cuanto a su composición de especies arbóreas se asociaron de forma positiva con una mayor riqueza de aves forestales. Finalmente, se proporcionan directrices de gestión para la planificación forestal que permitan promover la diversidad ornítica en esta región de la Europa mediterránea.

Improving interMediAte Risk management. MARK study

Background: Cardiovascular risk functions fail to identify more than 50% of patients who develop cardiovascular disease. This is especially evident in the intermediate-risk patients in which clinical management becomes difficult. Our purpose is to analyze if ankle-brachial index (ABI), measures of arterial stiffness, postprandial glucose, glycosylated hemoglobin, self-measured blood pressure and presence of comorbidity are independently associated to incidence of vascular events and whether they can improve the predictive capacity of current risk equations in the intermediate-risk population. Methods/Design: This project involves 3 groups belonging to REDIAPP (RETICS RD06/0018) from 3 Spanish regions. We will recruit a multicenter cohort of 2688 patients at intermediate risk (coronary risk between 5 and 15% or vascular death risk between 3-5% over 10 years) and no history of atherosclerotic disease, selected at random. We will record socio-demographic data, information on diet, physical activity, comorbidity and intermittent claudication. We will measure ABI, pulse wave velocity and cardio ankle vascular index at rest and after a light intensity exercise. Blood pressure and anthropometric data will be also recorded. We will also quantify lipids, glucose and glycosylated hemoglobin in a fasting blood sample and postprandial capillary glucose. Eighteen months after the recruitment, patients will be followed up to determine the incidence of vascular events (later follow-ups are planned at 5 and 10 years). We will analyze whether the new proposed risk factors contribute to improve the risk functions based on classic risk factors. Discussion: Primary prevention of cardiovascular diseases is a priority in public health policy of developed and developing countries. The fundamental strategy consists in identifying people in a high risk situation in which preventive measures are effective and efficient. Improvement of these predictions in our country will have an immediate, clinical and welfare impact and a short term public health effect