Research On Communication Signal Waveform Technology Of Aerospace TT&C Network Based On Automatic Machine Learning

In recent years,due to the development of communication measurement and control technology and the rise of artificial intelligence technology,the construction of intelligence and informatization has become the main direction of China’s wireless communication application field in the future.However,due to the increasingly scarce spectrum resources,the interference in the communication environment is complex and changeable.The development of cognitive dynamic technology system(Cognitive Dynamic System,CDS)also provides a solution for wireless communication technology,so that the adaptive network can avoid the influence more effectively under the change of target characteristics or external conditions,and enhance its Anti-interference ability.With the development of technology,artificial intelligence uses a large number of structured data mining methods to fit nonlinear information systems.However,its requirements for professional knowledge are getting higher and higher,and it cannot make full and effective use of existing artificial intelligence technology.Automatic machine learning(AutoML),the emergence and widespread popularization of this new artificial intelligence technology,will significantly lower the threshold of artificial intelligence technology,greatly expand the application field and popularization of artificial intelligence,and make artificial intelligence easy to use.There are generally two types of parameters in the learner model,one type can be learned and estimated from the data,and the other type of parameters cannot be estimated from the data,and can only be designed and specified based on human experience,and the latter become hyperparameters.Automated Machine Learning aims to simplify the process of generating models in machine learning by automating common steps such as data preprocessing,model selection,and tuning hyperparameters.AutoML refers to trying not to set hyperparameters through humans,but to use some kind of learning mechanism to adjust these hyperparameters.This paper considers the relationship between communication signal waveform parameters and environmental SNR and bit error rate in aerospace measurement and control scenarios,and proposes to use polynomial regression algorithm to build an adaptive parameter learning model,and map low-dimensional features to high-dimensional space through polynomial regression,while using the hyperparameter optimization method of grid search to find the optimal combination of hyperparameters in the training process of polynomial regression.In this paper,automatic machine learning technology is used.The two characteristics of training data are waveform pulse width and environmental signal-to-noise ratio.The selection steps of hyperparameters are also studied and analyzed on the prediction performance and prediction error.The simulation results also show that the automatic machine learning algorithm can automatically select the optimal hyperparameters,and the predicted value is closer to the theoretical value,and it also effectively reduces the complexity of code execution.