Networks LTE(4G) and Wi-Fi complementarity establish a heterogeneous system of wireless and mobile networks. We study and analyze the optimal performances of this heterogeneous system based on the bit rate, the blocking probability and user connection loss. Random Waypoint is the user mobility model. User's provided mobile terminal equipped with multiple accesses interfaces. We have developed a Markov chain to estimate the performances obtained from the heterogeneous networks system, which allowed us to propose an average bit rate value in a sub-zone of this system then the average blocking probability user connection in this zone. We have also proposed a sensitivity factor of maximal decrease of these selection network parameters. This factor informs about the heterogeneous networks congestion and dis-congestion system.
Integration of wireless and mobile networks to new generations constitutes a heterogeneous system, this is the case of LTE and WiFi networks. In this paper, we study and analyze the optimal performances of this system regarding the SINR(Signal Interference Noise Ration), the blocking probability and the user communication loss. The user mobility is represented by random wayPoint(RWP) model and users terminals equipped with multiple accesses interfaces. We have established a Markov chain to assess and analyze the performances obtained from the heterogeneous networks system. So we have proposed an average value of the signal power emitted in down-ling voice, the blocking probability of system connections.
The allocation of airport time slots depends on the configuration of the airport, particularly on that of the runways. Thus, in order to allocate the slots optimally in an airport platform, we proposed two optimization models in this article. The first model maximizes the airlines companies demands in the periods by taking into account the characteristics of the airport. This model assigns the flights' demands. It allows determining the number of demands which we can satisfy in a given period of amplitude of one hour. It also helps to incorporate dynamically the unmet demand of j period to the j+1 period.The second model aims to schedule the confirmed requests by the first assigning model. We are interested in the optimal repartition of the confirmed requests, while minimizing the flights delays. These models are used to optimize the air-traffic management of the Diass airport. Therefore, we have developed and implemented two algorithms for the resolution of these two models. The ressults of experimentations in Cplex show that our algorithms are efficient comparing to those obtained by the reference works existing in this field. The data used are those of the International Blaise Diagne Airport (AIBD).
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