CTGAN-assisted CNN for high-resolution wireless channel delay estimation

The estimation accuracy of first-arrival-path (FAP) delay plays a vital role in positioning performance. We investigate the limitations of traditional cross-correlation (CC) algorithms in delay estimation. Our work proposes a FAP delay estimation mechanism using conditional tabular generative adversarial network (CTGAN) assisted convolutional neural network (CNN). The mechanism uses the CC algorithm to extract the delay feature in the wireless signal as input and finally outputs the FAP delay. For communication scenarios where it is difficult to obtain a large amount of training data, we use CTGAN to assist CNN training to improve the accuracy of FAP delay estimation. A series of simulation experiments were presented to evaluate the performance of CTGAN-assisted CNN and compare it with traditional high-resolution delay estimation algorithms. The results show that CNN performs well in weak LOS signals and dense multipath situations. It can still maintain high precision in the case of insufficient data.