Method and device for determining community user perception

Abstract

The invention discloses a method and equipment for determining cell user perception. The method comprises the following steps: acquiring signaling plane data and user plane data of a terminal within a first predetermined time period when the terminal resides in a cell; acquiring the reference signal received power (RSRP) of the cell within the first predetermined time period; obtaining first index data based on the signaling plane data and the user plane data; obtaining second index data based on the RSRP; and determining whether the cell is a low-quality cell within the first predetermined time period based on the first index data and the second index data. According to the method and equipment disclosed by the invention, at least the service perception of the terminal can be enhanced, and the optimization of the network can be improved.

Description

technical field [0001] The invention relates to cell user perception technology, in particular to a method and equipment for determining cell user perception. Background technique [0002] In the Long Term Evolution system (LTE, Long Term Evolution), the perception capability of the terminal in the current cell can be measured by measuring the drop rate and network speed in the MR report through the network. It is also possible to obtain the good or bad perception ability of the terminal in the current community by counting the proportion of user complaints in the current community; It is determined that the perception ability of the terminal in the current cell is poor, that is, the current cell is a cell of poor quality. Wherein, with regard to the first method, there is a situation that a certain cell has a relatively low dropout rate and a high number of complaints. In the second method, if some users have poor user perception due to their own terminals, once a complai...

AI Technical Summary

Problems solved by technology

Among them, for the first method, there is a case where a certain community has a low dropout rate and a high number of complaints

The second method, if there are individual users who have poor user perception due to their own terminals, once a complaint is received, it will affect the complaint ratio of 10,000 users in the community, and then affect the final user perception evaluation result of the community

It can be seen that only based on the dropout rate in the MR report, or only based on the single index data such as the complaint ratio per 10,000 users, cannot truly reflect the perception of terminal services, and thus cannot improve network optimization.

Images