A method and device for network self-optimization

Abstract

The invention relates to the communication field and more particularly, to a method and an apparatus for network self-optimization with the object to improving the refinement for network optimization, to increasing the optimization efficiency and to reducing optimization cost. The method comprises: dividing the serviced cells into a plurality of sub-areas; through a user terminal at any position of the serviced cells, reporting its geographical position identifications in the sub-areas and the wireless evaluation indexes; based on the geographical position identifications, comparing the wireless evaluation indexes with the corresponding wireless evaluation index's standard values; and when the wireless evaluation index's standard values are not reached, re-configuring the sub-areas correspondingly based on the wireless configuring information corresponding to the wireless evaluation index's standard values. In this manner, the network side could optimize the network performance to its best state, therefore, reducing the human intervention and achieving the purpose of smart optimization of the network. The daily network optimization working efficiency is increased and at the same time, the operation cost is saved.

Description

technical field [0001] The invention relates to the communication field, in particular to a method and device for network self-optimization. Background technique [0002] Under the existing technology, whether the network indicators of the communication network can maintain high quality for a long time basically depends on the size of the network optimization and maintenance team. The more people involved in network optimization, the better the network indicators will be optimized. Especially in the initial stage of network construction, network optimization is often carried out using "crowd tactics". Daily network optimization basically uses manual optimization methods, and routine optimization work usually requires the cooperation of multiple people. [0003] For example, if the network indicators in a certain area are abnormal, causing user complaints, but the user information is not sure, you need to manually obtain the network indicators from the network management sys...

AI Technical Summary

Problems solved by technology

[0006] 1. The manual network optimization method needs to maintain a certain-scale optimization team. The conventional optimization test work often requires multiple people to cooperate to complete the test, which requires a lot of manpower and high operating costs.

[0007] 2. Using the artificial network optimization method, due to the consideration of manpower input costs, it is impossible to guarantee coverage in all areas, indoor areas or small places and other areas cannot be covered, so the network optimization cannot reach a fine level

[0009] 4. With the artificial network optimization method, at present, the front-end data analysis can only be collected through the dedicated drive test terminal, but the intelligent commercial terminal data cannot be collected through the background system to complete the drive test optimization work

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