Optimizing method and device of antenna feeder parameters

Abstract

The invention discloses an optimizing method and device of antenna feeder parameters and relates to a communication technology. The optimizing method comprises the following steps of: generating a Thiessen polygon of a cell according to an optimized cell list and geographic information data, which are pre-set; and monitoring a covering target value of each cell sampling point in a monitoring time interval so as to determine the cell having an antenna feeder parameter configuring problem; and furthermore, adjusting the antenna feeder parameters of the cell having the antenna feeder parameter configuring problem. Road test estimation does not need to be repeatedly carried out and the efficiency of optimizing the antenna feeder parameters is improved.

Description

technical field [0001] The present invention relates to communication technology, in particular to an antenna feeder parameter optimization method and device. Background technique [0002] Antenna optimization is a key part of network optimization. With the continuous construction of new base stations in the TD-SCDMA (Time Division Synchronized Code Division Multiple Access) network, or with the TD-SCDMA For network optimization, if it is difficult to make timely adjustments to the antenna parameter settings through antenna feeder optimization, it will not only fail to suit the actual situation of the network, but also cause interference, switching problems, and edge coverage problems when the antenna parameters are not set properly. As a result, the call loss rate in the network is high, the call retention rate is low, and the handover success rate is low. At the same time, antenna-feeder optimization is carried out for each sector pair, the number is huge, and the work is...

AI Technical Summary

Problems solved by technology

[0002] Antenna optimization is a key part of network optimization. With the continuous construction of new base stations in the TD-SCDMA (Time Division Synchronized Code Division Multiple Access) network, or with the TD-SCDMA For network optimization, if it is difficult to make timely adjustments to the antenna parameter settings through antenna feeder optimization, it will not only fail to suit the actual situation of the network, but also cause interference, switching problems, and edge coverage problems when the antenna parameters are not set properly. Lead to high call loss rate, low call hold rate and low handover success rate in the network

At the same time, antenna-feeder optimization is carried out for each sector pair, the number is huge, and the work is cumbersome. If the traditional "drive test->assessment->drive test..." cycle process is used, the time and effort in the engineering optimization process will be increased. cost

In addition, the balance between network quality and efficiency must be taken into account, which makes the configuration strategy very complicated

[0003] Specifically, in the network opening stage, due to the complex wireless environment, it is difficult to ensure the optimal downtilt angle, azimuth angle and pilot channel transmission power, so it is difficult to obtain the established network performance indicators with the least number of base stations; in the network operation and maintenance stage, According to the cycle process of "drive test->assessment->drive test...", it is difficult to guarantee the effect of antenna feeder optimization due to the heavy workload.

[0004] In the patent "CN201010583988.0", a method for adjusting the antenna feeder parameters by using the wireless network coverage simulation system suitable for the TD-SCDMA system is proposed. According to the simulation results, the wireless coverage in the area is evaluated, and the antenna feeder adjustment is given. The scheme; the optimization of antenna parameters through this scheme requires electronic maps and propagation models that can accurately and finely reflect the geomorphology and architectural features of the current area, combined with the simulation evaluation of these data, it can be seen that the simulation evaluation will directly affect the sky The degree of optimization of the antenna feeder, while the complexity of the actual network directly affects the simulation evaluation results, making it difficult to predict the guidance of the final antenna feeder parameter optimization results for the live network

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