Network Site Optimization and Clustering Study Based on Simulated Annealing Algorithm

Nowadays, communication networks are becoming increasingly complex. This paper aims to demonstrate an effective method to achieve the intelligent planning for network base stations (BSs). The various parameters such as BS coordinates (x, y), the collaboration of multiple types of BS, and the density of BS construction are taken as design parameters for BS placement. We construct the objective function using the lowest total cost and the total minimum workload of BS to 90%. To solve the problem of siting planning with large data volume and mixed placement of multiple BS, we propose a new practical three-step model for BS siting planning: (I) roughly selecting the alternative coordinates for the BS using the DBSCAN algorithm; (II) correcting and further refining the alternative BS coordinates using the K-means algorithm; (III) determining the optimal BS construction solution to meet the requirements using simulated annealing algorithm (SAA). The real data of a
$2500\times 2500$
area have been used for the simulation test. The simulation result shows that BS placement covers 90.03% of the workload, confirming that the proposed method can handle site planning for large orders of magnitude of data and use a mix of BS to achieve the best economics for the demand. This paper provides basic support for future research on network site optimization.