On Performance Optimization in Self-Organizing Network using Enhanced Simulated Annealing with Similarity Measure

Network performance optimization is among the most important tasks within the area of wireless communication networks. In a Self- Organizing Network (SON) with the capability of adaptively changing parameters of a network, the optimization tasks are more feasible than static networks. Yet, with an increase of OPEX and CAPEX in new generation telecommunication networks, the optimization tasks are inevitable. In this paper, it is proven that the similarity among target and network parameters can produce lower Uncertainty Entropy (UEN) in a self-organizing system as a higher degree of organizing is gained. The optimization task is carried out with the Adaptive Simulated Annealing method, which is enhanced with a Similarity Measure (SM) in the proposed approach (EASA). The Markov model of EASA is provided to assess the proposed approach. We also show a higher performance through a simulation, based on a scenario in LTE network