Radio Map Reconstruction Based on Nas Enhanced Deep Regularization Completion for Uav Communications

This paper proposes a radio map reconstruction method based on the Neural Architecture Search Enhanced Deep Regularization Model. Traditional radio map reconstruction algorithms face many challenges, such as extremely sparse measurement data and complex wireless signal environments. To solve the problem of unstable output, an external regularizer is introduced to provide additional regularization input in the prediction process to assist the neural network in capturing the explicit prior information from the sparse measurements and performing restoration. Moreover, to ensure the efficiency of capturing implicit prior information, neural architecture search is adopted to optimize the structure of the completion network, further enhancing the robustness of the neural network and the accuracy of reconstruction. The experimental results show that our proposed method is more accurate and stable than the traditional completion methods, especially in the circumstance of a low sampling rate, and can better adapt to the situation of interrupting the probability distribution in unknown regions without a dataset.