Big Data-based Self Optimization Networking in Multi Carrier Mobile Networks

With regard to the increasing growth of demands in order to provide various services and high-speed in new generation of mobile telecommunications, one of the challenges ahead, are the available limited resources. In early cellular systems, with regard to the type of services that were provided, techniques such as the use of this frequency were enough. But in the telecommunications of the next generation, we have to use different techniques and features that are provided with resources in the area covered by the network, leading to an exponential increase in the operational complexity of these kinds of networks will be optimized in the scheme and optimization point of view. Therefore, to achieve an acceptable level of performance on these networks, intercellular performance management will be of great importance. One of the techniques that are recommended to enhance further the efficiency of cellular networks is the usage of self-optimization techniques in these kinds of networks. The foundation of our proposed scheme is presenting a self-optimizing model based on neighboring indices by which we can make feasible the possibility of controlling resources and the respective indices relevant to neighboring connections of cellular network without the interference of human force and merely by relying on the network’s intelligence. In order to better the efficiency of the intended scheme, we used the Big Data technique to analyze the data and network’s better decision-making process in allocating resources. In a way that in the uplink direction, user’s data from the user layer to the network core are analyzed in one register and based on semantic information extracted from these data, the decision-making center will be able to allocate resources in a more intelligent way. The works done on this Category to provide distributed solution of non-self-optimization or has paid for resources allocation that also may not to be close to the optimized solution or have passed up the effect of changes of network conditions. Gained results in the use of this model represent more efficient use of network resources and also reducing the load rate of the imposed signalling on the network.