With the rapid growth of a network scale and user requirements, using air interface transmission technologies and network management optimization technologies to improve performance and efficiency of a wireless network has become a key to successful operation of a wireless network. At present, advanced air interface transmission technologies such as the OFDMA, multi-antenna transmission, and various channel coding and decoding technologies have made single link performance very close to a Shannon limit, and system-level network optimization technologies will be a key to improving network performance. On the other hand, to improve network efficiency, a network configuration needs to adapt to a network environment change. Therefore, during network optimization, it is required to know the network environment change. Network environment changes at different time granularities may be perceived by using different sampling frequency, and therefore a change rule can be discovered and proper network optimization technologies and algorithms are used.
Existing network optimization methods can be classified into a centralized optimization method and a distributed optimization method. An optimization decision of the centralized network optimization method is made at a centralized control point. The centralized control point is connected to all cells to collect measurement values of all the cells, determines an optimization plan according to the measurement values of all the cells, and updates corresponding configurations of all the cells; therefore, performance of an entire network can be improved. An optimization decision of the distributed network optimization method is made in each cell. Each cell determines, according to a detection value of the cell, an optimization plan used for optimizing the cell, and updates a corresponding configuration of the cell; therefore, performance of an entire network can be improved. If the centralized optimization method is used, sending, by all cells in the network, measurement values to the centralized control point generates great overheads. Therefore, the centralized optimization method usually takes into consideration a change at a large time granularity, and an optimization effect is reflected in a network performance index of a large time granularity. In contrast, an advantage of the distributed optimization method is that a change at a small time granularity may be taken into consideration, and an optimization effect is reflected in a network performance index of a small time granularity.
In an existing network, optimization of some key performance indexes (Key Performance Index, KPI) affects some other KPIs. Therefore, how to jointly optimize KPIs is a problem urgently needing to be resolved. However, the distributed optimization method usually takes into consideration only on a KPI of a small time granularity, and the centralized optimization method usually takes into consideration only on a KPI of a large time granularity; therefore, the KPI of a large time granularity and the KPI of a small time granularity cannot be optimized jointly.