MR coverage influence factor determining method based on random forest
A technology of influencing factors and random forest, applied in the field of big data processing and machine learning, can solve problems such as insufficient accuracy, doubtful effectiveness, and inability to judge the accuracy rate, and achieve the effect of strong stability and high precision
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[0043] Below in conjunction with accompanying drawing of description, the present invention will be further described.
[0044] The present invention provides a random forest-based MR coverage factor determination method, such as figure 1 and figure 2 shown, including the following steps:
[0045] 1) Select several relevant dimensions that affect MR coverage, as shown in Table 1.
[0046] Several relevant dimensions that affect MR coverage include working frequency band, number of carrier frequencies, coverage type, longitude, latitude, maximum transmit power, number of weak coverage sampling points, total number of sampling points, proportion of weak coverage sampling points, and channel number of the center carrier frequency , Whether it is an uplink interference cell, whether it is an uplink weak coverage cell, whether it is an over-coverage cell, station height, cell individual offset, frequency band indication, electronic downtilt, mechanical downtilt, azimuth, antenna...
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