At present, super-resolution reconstruction of a single image achieves a good effect, but most models achieve the good effect by increasing the number of network layers rather than exploring the correlation between channels. In order to solve this problem, an image super-resolution reconstruction method based on Channel Attention mechanism (CA) and Depthwise Separable Convolution (DSC) was proposed. The multi-path global and local residual learning were adopted by the entire model. Firstly, the shallow feature extraction block was used to extract the features of the input image. Then, the channel attention mechanism was introduced in the deep feature extraction block, and the correlation of the channels was increased by adjusting the weights of the feature graphs of different channels to extract the high-frequency feature information. Finally, a high-resolution image was reconstructed. In order to reduce the huge parameter influence brought by the attention mechanism, the depthwise separable convolution technology was used in the local residual block to greatly reduce the training parameters. Meanwhile, the Adaptive moment estimation (Adam) optimizer was used to accelerate the convergence of the model, so as to improve the algorithm performance. The image reconstruction by the proposed method was carried out on Set5 and Set14 datasets. Experimental results show that the images reconstructed by the proposed method have higher Peak Signal-to-Noise Ratio (PSNR) and Structural SIMilarity index (SSIM), and the parameters of the proposed model are reduced to 1/26 of that of the depth Residual Channel Attention Network (RCAN) model.

Channel congestion occurs when the vehicular traffic density increases to a certain extent in Internet of Vehicles (IoV), even if there are only beacons in the wireless channel. To solve the problem, a Distributed-Weighted Fair Power Control (D-WFPC) algorithm was proposed. Firstly, considering the actual channel characteristics in IoV, the Nakagami-m fading channel model was used to establish the random channel model. Then, the mobility of the nodes in IoV was considered, and a power control optimization problem was established based on the Network Utility Maximization (NUM) model, which kept the local channel load under the threshold to avoid congestion. Finally, a distributed algorithm was designed by solving the problem with dual decomposition and iterative method. The transmit power of each vehicle was dynamically adjusted according to the beacons from neighbor vehicles. In the simulation experiment, compared with the fixed transmit power schemes, the D-FWPC algorithm reduced the delay and packet loss ratio effectively with the increase of traffic density, the highest reduction was up to 24% and 44% respectively. Compared with the Fair distributed Congestion Control with transmit Power (FCCP) algorithm, the D-FWPC algorithm had better performance all the way and the highest reduction in delay and packet loss ratio was up to 10% and 4% respectively. The simulation results show that the D-WFPC algorithm can converge quickly and ensure messages to be transmitted with low delay and high reliability in IoV.