A network load balancing optimization method based on user data

Abstract

The invention discloses a network load balancing optimization method based on user data. The network load balancing optimization method based on user data comprises the following steps: (1) extractingand screening KPI indexes of high-load cells: extracting KPI indexes of the cells, calculating the load rate of the cells according to each index, and screening out all the high-load cells; 2) acquiring data of high-load district and user thereof: collect data of wireless network carrying user location reported by user intelligent terminal; (3) user data and algorithm operation: putting the userdata obtained in the step (2) and the high-load data in the step (1) into the model, calculating and adjusting parameters through constant iteration and outputting a high-load cell balance optimization scheme; (4) performing scheme execution: the output scheme is executed, and the closed loop ends after the execution is completed. The method is accurate, saves the human cost, and effectively usesthe system resources to increase the system capacity and improve the stability of the system.

Description

technical field [0001] The invention belongs to the technical field of the wireless communication industry, and in particular relates to a network load balancing optimization method based on user data. Background technique [0002] The 4G network has completed comprehensive geographical coverage, marking the advent of the era of high-speed wireless communication. Extensive coverage and good user experience have brought rapid growth in the number of 4G users. Therefore, in terms of network construction and optimization, the focus of attention has also shifted from extensive coverage to deep coverage. Especially in hotspot areas such as colleges and universities, stations, and shopping malls, due to the particularity of the scene, the user base is huge, and the business volume is also increasing, which leads to the high load of the covered cells in this scene, resulting in dropped calls, handovers, Problems such as congestion will directly lead to a decline in user perception...

AI Technical Summary

Problems solved by technology

[0005] However, the adjustment parameter strategy obtained by manual analysis is based on optimization experience and is not precise enough.

[0006] Manual analysis is more suitable for cells that have been in a congested state for a long time. Since mobile users in an LTE cell are in a state of dynamic change in the time dimension, correspondingly, the congestion state of the cell is also dynamic. Manual analysis and manual adjustment are not only inefficient and costly , and the use of empirical values ​​is not accurate enough, and the measurement cannot meet the timeliness requirements for capacity optimization and adjustment

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