Learning-Based Health Prediction Method for Airborne DME Receiver with Signal Processing Techniques in 6G Networks

Abstract

Accurate aircraft positioning is the key to construct a reliable network topology when aircrafts are used to assist 6G cellular networks in ground communications. Distance Measuring Equipment (DME) has been widely used in aircraft positioning with the help of multiple ground-based radar stations. In this paper, a learning-based health prediction method for airborne DME receiver is proposed by using signal processing techniques to achieve quantitative health status assessment and failure degradation trend prediction, when the DME is used to measure the distance between ground-based radar stations and airborne DME. First, a quantitative airborne DME device receiving channel health evaluation model is established, which takes the Automatic Gain Control (AGC) attenuation value and the collected distance between the ground beacon station and the airborne DME receiver with DME device as input, to calculate the receiving channel AGC attenuation value deviation and gain loss. The model can be used to build the mapping relationship between the receiver channel gain loss and the DME function range, and further establish the calculation model of the receiving channel’s health index. Second, a multi-model fusion fault prediction framework based on the Deep Belief Network (DBN) techniques is proposed. In this framework, the problem of insufficient generalization and robustness of the traditional DBN model is solved by introducing the Dropout mechanism into the DBN structure, and an improved weighted voting method is utilized as a model fusion algorithm to eliminate the deviation of prediction results caused by environmental load differences and improve the accuracy of fault prediction. Finally, extensive experiments are conducted to show the feasibility of the proposed method, and the results show that the proposed method has a good performance.