Coverage and capacity optimization based on tabu search in ultra-dense network

Abstract

To meet the requirements of high system capacity and coverage of 5G network, ultra-dense network is viewed as the key technology for networking evolution. And for densely deployed small cell network, self-optimization is crucial in the aspect of reducing the cost of network management while optimizing the network performance. This paper focuses on the coverage and capacity optimization, proposing a mathematical combined optimization function to balance the conflicting key performance indicators. And under this model, we propose the tabu search algorithm for generating new antenna transmit power to optimize the performance. Simulation results show that our proposed algorithm gets significant improvement in network performance and outperforms the adaptive simulated annealing in convergence speed while optimizing.