Research and Application of Intelligent Antenna Feeder Optimization System based on Big Data

The stability of passive antenna feed operation is an important indicator to measure the quality of wireless network. On the basis of big data of antenna and feed fault, this paper proposes a support vector machine (SVM) based fault classifier of antenna and feed, in order to quickly classify the faults of antenna and feed system (AFS). In addition, the improved Cascaded Pyramid Network (CPN) learning algorithm is employed to establish a fault diagnosis device of antenna and feed to quickly diagnose various categories of faults. For the fault model of antenna and feed, we continue to learn and train to optimize the fault classifier model, as well as the fault diagnosis model. For the fault diagnosis information, the antenna and feed fault classifier is used to update the classified faults, which empower the antenna and feed fault classification more accurate.