Antenna azimuth diagnosis based on multi-layer perceptron model

Abstract

Antenna azimuth was seen as a key factor in the quality of mobile communications, and its accuracy will directly affect the quality of network optimization.An antenna azimuth diagnosis method was proposed based on multi-layer perceptron.The azimuth was divided into 12 interval classes, each class covered a range of 30°, that was,[0, 30°) was recorded as class 0, …,[330°, 360°) was recorded as class 11.The multi-layer perceptron algorithm was used to identify the range of the antenna azimuth angle and automatically identify the angle range of the antenna azimuth angle, which provided effective data support for the network optimization engineer to determine the actual network coverage problem, and greatly reduced workload and labor cost in verifying antenna performance.Experimental results show that the method can effectively and quickly discriminate the antenna azimuth interval class, and the recognition accuracy reaches 92.6%, which is higher than the classification accuracy of random forest and logistic regression classification algorithms.

Keywords

• antenna azimuth angle
• multi-layer perceptron
• back propagation algorithm
• neural network
• network optimization