Image super-resolution (SR) technology is crucial for applications such as telemedicine. Performing image SR tasks using semantic communication allows the received information to be directly applied without the need for complex operations at the receiver. However, research still grapples with the challenges of constructing a joint source-channel coding (JSCC) method that effectively extracts semantic information from LR images for reconstructing HR images, as well as reducing the unfavorable impact of the wireless channel. In this paper, we propose a deep semantic information extraction (DSIE) module for the image SR task in semantic communications. This module ingeniously integrates the advantages of convolutional neural network (CNN) and Transformer architectures and incorporates signal-to-noise ratio (SNR) to dynamically adjust the output via the channel attention mechanism. Building on DSIE, we custom-design the JSCC scheme for image SR tasks in semantic communications. Simulation results confirm the effectiveness of our proposed DSIE and show that the proposed JSCC scheme surpasses the existing DNN-based JSCC schemes and the traditional separated-source channel coding schemes.

With the development of the Internet of Things (IoT) technology, massive amounts of sensor data in applications such as fire monitoring need to be transmitted to edge servers for timely processing. However, there is an energy-hole phenomenon in transmitting data only through terrestrial multi-hop networks. In this study, we focus on the data collection task in an unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) network, where a UAV is deployed as the mobile data collector for the ground sensor nodes (SNs) to ensure high information freshness. Meanwhile, the UAV is equipped with an edge server for data caching. We first establish a rigorous mathematical model in which the age of information (AoI) is used as a measure of information freshness, related to both the data collection time and the UAV’s flight time. Then a mixed-integer non-convex optimization problem is formulated to minimize the peak AoI of the collected data. To solve the problem efficiently, we propose an iterative two-step algorithm named the AoI-minimized association and trajectory planning (AoI-MATP) algorithm. In each iteration, the optimal SN–collection point (CP) associations and CP locations for the parameter $\epsilon$ are first obtained by the affinity propagation clustering algorithm. The optimal UAV trajectory is found using an improved elite genetic algorithm. Simulation results show that based on the optimized $\epsilon$, the AoI-MATP algorithm can achieve a balance between data collection time and flight time, reducing the peak AoI of the collected data.