Enhancing the DBSCAN and Agglomerative Clustering Algorithms to Solve Network Planning Problem

With existing telephone networks nearing saturation and demand for wire and wireless services continuing to grow, telecommunication engineers are looking at technologies that will deliver sites and can satisfy the required demand and grade of service constraints while achieving minimum possible costs. The city data is given as a map of streets, intersection nodes coordinates, distribution of the subscribers´ loads within the city and the location of base station in mobile network in this city. The available cable sizes, the cost per unit for each size and the maximum distance of wire that satisfied the allowed grade of service. NetPlan (network planning package) is developed in the spirit of DBSCAN and agglomerative clustering algorithms. In this paper we studied the problem of congestion in multi service access node (MSAN) due to the increasing the number of subscribers which cause degradation in grade of service and in some time impossible to add new subscribers. The NetPlan algorithm is introduced to solve this problem. This algorithm is density-based clustering algorithm using physical shortest paths available routes and the subscriber loads. In other hand decreasing the cost also is our deal in this paper so in the second phase in clustering process we modify the agglomerative algorithm that merge the neighboring cluster which satisfying certain condition. Experimental results and analysis indicate that the combination to algorithms was effective, leads to minimum costs for network construction and make the best grade of service.