Clima social escolar: concepto e importancia.

The mismatch between the increasing demands of wireless spectra and limited radio resources poses an imminent challenge in efficient spectrum sharing and subsequent wireless network de-sign. To this end, Next Generation wireless networks are considered necessary for the support of emerging services with their increasing requirements. More importantly, NGN will typically be characterized by a distributed, dynamic, self organising architecture [8]. However, the emergence of Cognitive Radio Networks and other associated networks (Ad hoc/Mesh, Sensor networks, LTE, etc), which fall in the category of NGN wireless networks, have given rise to many open is-sues in network design. Consequently more and more researchers are concentrating their efforts in the design of these future networks [174]. To this end, a fundamental approach during the design and engineering of these NGN communication systems, protocols, algorithms and architectures, is the estimation of their performance, as well as understanding and visualization of the micro and macro behaviour of the systems and their components. Classically, this can be achieved by applying three different methodologies, firstly experiments with real systems and prototypes, sec-ondly analytic techniques and thirdly simulation. Figure 3.1 shows the methodological approach adopted in the design and optimisation of our Cognitive Radio Mesh network.

Figure 3.1: Methodology

This chapter is thus structured as follows. Section 3.1deals with analytic modelling wherein three important theoretic basis for the thesis are optimisation techniques such as game theory, Lagrange

Multiplier, and Genetic algorithms. Section 3.2 presents the TVWS measurement system in the form of hardware and software components. Ultimately, section 3.3 gives an insight into the simulation efforts that are relevant to the thesis.

3.1 Analytic Modelling

Analytic tools for network design are currently in the early stages of development. In many ways efforts are at the stage of creating network models which contain enough salient features of the network so that the behaviour of such networks may be predicted from the model behaviour [148]. Within the context of this thesis, several issues need to be taken into consideration for the design of an efficient dynamic spectrum sharing cognitive mesh network and these are:

the unreliable and broadcast nature of wireless channels, user mobility and dynamic topology, various network infrastructures, and, most importantly, network users’ behaviour. To this end, our analytic modelling leverages game theory, a discipline which strives to model situations in which decision makers have mutual possibly conflicting interests. Moreover, though game theory was initially developed for economics, it is also being extended to the wireless networking design domain. This is because the cooperative and competitive environments in our envisaged cognitive radio mesh network naturally lend themselves to a game theoretic formulation. Furthermore efficient dynamic spectrum sharing can only be achieved if the network users’ intelligent behaviours and interactions are to be thoroughly studied and analyzed. Subsequently game theory is an excellent match in nature to dynamic spectrum sharing problems. The importance of studying dynamic spectrum sharing in a game theoretical framework is manifold. Firstly, by modelling dynamic spectrum sharing among network users (Primary and Secondary users) as games, the network users’ behaviours and actions can be analyzed in a formalized game structure, by which the theoretical achievements in game theory can be fully utilized. Secondly, game theory equips us with various optimality criteria for the spectrum sharing problem. Specifically, the optimization of spectrum usage in Dynamic Spectrum Access Networks (DSANs) is generally a multi-objective optimization problem, which is very difficult to analyze and solve. Game theory provides us with well defined equilibrium criteria to measure game optimality under various game settings (network scenarios in our context). Thirdly, non-cooperative game theory, one of the most important game theories, enables us to derive efficient distributed approaches for dynamic spectrum sharing using only local information. Such approaches become highly desirable when centralized control is not available or flexible self-organized approaches are necessary. The game in a strategic form is examplified by an oligopoly. Examples of oligopolies include Cournot, Stackelberg(see Figure3.2) and Bertrand.

Figure 3.2: Stackelberg Game between Producers [156]

3.1.1 Lagrange Multiplier. In the analytic modelling phase, the Lagrange multiplier method, named after Joseph Louis Lagrange, provides an alternative method for the constrained non-linear optimization problems. The method is deployed as a strategy to determine the local maxima and minima of a function subject to either equality or inequality constraints [157].

3.1.2 Genetic Algorithms. A Genetic Algorithm is an adaptive heuristic search algorithm that mimics the process of natural evolution. In the general case this type of algorithm is deployed when solving optimization and search problems. Thus a genetic algorithm is deployed as an optimization tool of the developed system (envisaged Cognitive radio mesh network) and an appropriate fitness function is developed [85]. Genetic Algorithms fall under the category of evolutionary algorithms which are used to generate the solution of the optimization problems using techniques of natural evolution like inheritance, selection, mutation, and crossover.

3.2 TV White Space Occupancy Measurement

The TV White Space measurement system is perceived as a system which comprises hardware and software components.

1. Hardware Component: In order to approach the problem of characterizing primary user activity, a tool for monitoring spectrum use was needed. The RF explorer shown in Figure 3.3is a low cost device which makes use of the energy detection sensing algorithm discussed in chapter 2in section 2.4.

ISO

LARGE PRODUCER

GAME

PRODUCERS

Figure 3.3: RF Explorer

Our low cost RF explorer has the ability to measure frequencies over a frequency range 240M hz−960M Hz, display full frequency spectrum in the band including carrier as well as modulated shape [289]. Furthermore the RF explorer device is able to display Spread Spectrum activity if its exist, show bandwidth to monitor collisions, frequency deviation from expected tone, etc. Moreover the RF Explorer model WSUB1G used in the measurement process was fitted with a Nagoya NA-773 wideband telescopic antenna, which has wide band measurement capability. Although a number of commercial instruments are available for this purpose, our choice for the RF explorer is motivated by its low cost which is about 120. Unlike other RF measurement instruments the RF explorer is fully functional as an independent unit though it can be interfaced with the computer for additional features.

2. Software Component: The RF Explorer for Windows PC Client application provides tradi-tional spectrum trace views along with more graphical waterfall views.

Figure 3.4: RF Explorer Display [1]

3.3 Simulation

The dynamic spectrum sharing problem is an excellent candidate for discrete event simulation (DES). Discrete event simulation is concerned with the simulation of some system which evolves through time [171]. In the general case, a discrete event simulation is used for systems where the state of a system changes in discrete points in time. A system can change in only a countable number of points in time. A time advance algorithm is relevant in DES and this requires an event list. Practically a simulation clock time will progress only up to that instant when the event is expected to occur. A simulation will generally estimate stochastic,dynamic and discrete model output. In this thesis, for the purpose of validating both our cooperative and non-cooperative analytic models, a discrete event simulator is to be built for the cognitive radio mesh network model. The discrete event simulator is built using C++. Using the simulator built in C++, we will conduct several simulation experiments to analyse the performance of a Time of Day Traffic Engineered (ToD TE) network for different network sizes in section 7.6 and subsection7.6.3