The Research And Application Of Gsm900/1800Dual_Band Network Traffic Balancing Automatic Optimization

GSM900/DCS1800Dual-Band Network is the main wireless access network for current2G wireless mobile networks. Under the current circumstance of running out of frequency resource on GSM900, the method of achieving traffic balancing in dual-band network helps to improve DCS1800M traffic capacity, deciding the network quality improvement and user experience. Therefore, the optimization on traffic balancing is critically needed for in-depth study.This paper studies the common optimization methods for current traffic balancing based on handover reselection parameters optimization on dual band network construction phase and post maintenance phase. These help to improve traffic balancing. However, due to the expansion of GSM900and DCS1800network, it requires collaboration based on coverage, interference and parameters of dual band network to solve the traffic balancing issue, so that gives the greater optimization improvement.This paper focuses on problem of traffic balancing in dual band network, performing in-depth study on genetic algorithm, Tabu search algorithm and mobile measurement result (MR) data to allow network optimization work closely with scientific algorithms, making the traffic balancing optimization in dual-band network can apply these technologies to obtain the effect rapidly. It also serves as foundation for designing an automatic optimization method with better efficient for dual-band network in the future.The paper presents an automatic optimization method to implement the optimization algorithm based on genetic algorithm and automatic reselection and handover parameters optimization algorithm based on MR data. These algorithms will be implemented on dedicated dual-band network optimization platform to complete the debugging for optimization platform. Based on part of GSM900/DCS1800network in a city as a network environment foundation, it applied the2algorithms and compares the result with the traditional approach, which highly relies on engineers’experience. The comparison shows the automatic optimization method is able to greatly improve the optimization efficiency, and the resulting a significant reduction in resources for mobile service operators.