The planning， deployment and optimization of mobile communication system networks all depend to varying degrees on the accuracy of the Reference Signal Receiving Power （RSRP） estimation. Traditionally， the RSRP of a signal receiver in a cell covered by a base station can be estimated by the corresponding wireless propagation model. In an urban environment， the wireless propagation models for different cells need to be calibrated using a large number of RSRP measurements. Due to the environment differences of different cells， the calibrated model is only applicable to the corresponding cell， and has low accuracy of RSRP estimation within the cell. To address these issues， the RSRP estimation problem was transformed into an image denoising problem and a cell-level wireless propagation model was obtained through image processing and deep learning techniques， which not only enabled RSRP estimation for the cell as a whole， but also was suitable to cells in similar environments. Firstly， the RSRP estimation map of the whole cell was obtained by predicting the RSRP of each receiver point by point through a random forest regressor. Then， the loss between the RSRP estimation map and the measured RSRP distribution map was regarded as the RSRP noise map， and a image denoising RSRP estimation method based on Conditional Generative Adversarial Network （CGAN） was proposed to reflect the environmental information of the cell through an electronic environmental map， which effectively reduced the RSRP of different cell. Experimental results show that the root mean square error of the proposed method is 6.77 dBm in predicting RSRP in a new cross-cell RSRP scenario without measured data， which is 2.55 dBm lower than that of the convolutional neural network-based RSRP estimation method EFsNet； in the same-cell RSRP prediction scenario， the number of model parameters is reduced by 80.3% compared with EFsNet.

In the process of driving， autonomous vehicles need to complete target detection， instance segmentation and target tracking for pedestrians and vehicles at the same time. An environment perception model was proposed based on deep learning for multi-task learning of these three tasks simultaneously. Firstly， spatio-temporal features were extracted from continuous frame images by Convolutional Neural Network （CNN）. Then， the spatio-temporal features were decoupled and refused by attention mechanism， and differential selection of spatio-temporal features was achieved by making full use of the correlation between tasks. Finally， in order to balance the learning rates between different tasks， the model was trained by dynamic weighted average method. The proposed model was validated on KITTI dataset， and the experimental results show that the F1 score is increased by 0.6 percentage points in target detection compared with CenterTrack model， the Multiple Object Tracking Accuracy （MOTA） is increased by 0.7 percentage points in target tracking compared with TraDeS（Track to Detect and Segment） model， and the \mathrm{A} {\mathrm{P}}_{\mathrm{50}} and \mathrm{A} {\mathrm{P}}_{\mathrm{75}} are increased by 7.4 and 3.9 percentage points respectively in instance segmentation compared with SOLOv2 （Segmenting Objects by LOcations version 2） model.