This paper describes the application of an evolutionary optimization method to design satellite constellation for continuous regional coverage without intersatellite links. This configuration, called mutual coverage, is related to some technical limitations that exist on small satellite technology. The coverage of the north Algerian seismological network is taken as an example of application. A Multi Objective Genetic Algorithm (MOGA) is used to make a trade-off between the improvement of the coverage rate, the minimization of the total number of satellites and the reduction of the satellites' altitude. First, some experiments have been performed to find the weight distribution of the fitness function that shows the most significant improvement of the average fitness function. Then, some optimized constellation designs are given for different ranges of altitude and it is shown that the size of the MOGA constellation design is significantly reduced compared to the traditional geometrical design.
This study introduces an original theoretical approach for rectangular microstrip antennas analysis and design. The purpose method consists in the coupling between artificial neural networks tool applied to the analysis of rectangular antennas and a tabu search for the optimization. A neural network acting as database will allow to reduce strongly the computation time. This method will be illustrated in four examples.
Abstract. The explosive growth of wireless services in recent years illustrates the growing demand for communications, so the spectrum becomes more congested. We know that static spectrum allocation is a major problem in wireless networks. Generally, these allocations lead to an inefficient use of spectrum. To solve the problem of congestion, cognitive radio networks use dynamic spectrum access. In this paper, we use a technique based on auctions theory known for its simplicity and facilitates the allocation of scarce resources.
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