Deep Learning and Fresnel Zone Theory for Enhanced Channel Prediction Using Adaptive Image Features

The advancement of communication technologies has imposed increasingly stringent requirements on the precision and generalization capabilities of channel models. Traditional statistical and deterministic models frequently encounter challenges in achieving an optimal balance between these two aspects. To address this challenge, this letter introduces a channel prediction model based on deep learning and Fresnel zone propagation theory to achieve accurate prediction of path loss (PL) and delay spread (DS). To improve the extraction of environmental information from environmental images for channel prediction, an enhanced image environmental feature representation method is proposed. This method adaptively adjusts the dimensions of the input image based on the distance between the transmitter (Tx) and receiver (Rx). Furthermore, an image importance distribution map (IIDM) is defined to delineate the variations in significance across the different regions of the environmental image. Subsequently, a model architecture capable of processing images of variable sizes is developed utilizing spatial pyramid pooling (SPP) techniques. Finally, using channel measurement data obtained at 5.9 GHz in Changsha, the performance of the proposed model is validated employing a leave-one-out cross-validation method. Compared to existing models, the proposed prediction model achieves a reduction in the average root mean square error (RMSE) for PL and DS prediction by approximately 12.78% and 8.2%, respectively.