Semantic communications, which aim to effectively convey the meaning of messages (such as text and images) rather than transmitting the exact messages themselves, have garnered widespread attention from industry and academia. A suitable joint source-channel coding (JSCC) scheme is crucial for semantic communication systems, as it can significantly improve system performance, such as communication reliability. Current research efforts primarily focus on employing various deep neural network (DNN) models, particularly the Transformer model, to design JSCC schemes. However, existing Transformer-based JSCC schemes usually exhibit a considerable number of model parameters and computational demands, limiting their real-world applicability. To address this challenge, we propose a novel DNN model based on DeLighT, a deep and lightweight variant of the standard Transformer, using a text semantic communication system (TSC) as an example. This proposed model enables a lightweight JSCC scheme for the TSC system. Through simulation results, we demonstrate that the proposed JSCC scheme achieves comparable or better communication reliability than the Transformer-based JSCC scheme while requiring significantly fewer parameters and smaller runtime.

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.

Unmanned aerial vehicle (UAV) networks face critical challenges in dynamic environments where conventional approaches treat trajectory optimization and resource allocation as separate problems, failing to capture their intricate interdependencies and leading to suboptimal performance, excessive energy consumption, and processing delays. This paper addresses these limitations through a novel hybrid methodology that uniquely integrates deep reinforcement learning with convex optimization for joint optimization. Our innovation lies in two interdependent algorithms: Deep Reinforcement Learning (DRL)-based relay UAV trajectory optimization algorithm (DRL-RUTOA), which leverages Model-Agnostic Meta-Learning for rapid environmental adaptation, and computation-aware multi-UAV trajectory optimization algorithm (CA-MUTOA), which employs a benefit-cost prioritization mechanism for selective computational offloading. Unlike previous approaches, we formulate a unified multi-objective optimization framework that simultaneously balances network throughput, energy efficiency, and computational task management. Simulation results demonstrate that our integrated approach significantly outperforms conventional methods, achieving a 35% improvement in network throughput, 28% reduction in processing delay, and 42% reduction in energy consumption. Additionally, our framework exhibits superior convergence efficiency, requiring only 15 iterations compared to 32-42 iterations for conventional methods, confirming its practical viability for resource-constrained UAV operations in complex mission environments.

Deploying federated learning at the wireless edge introduces federated edge learning (FEEL). Given FEEL’s limited communication resources and potential mislabeled data on devices, improper resource allocation or data selection can hurt convergence speed and increase training costs. Thus, to realize an efficient FEEL system, this paper emphasizes jointly optimizing resource allocation and data selection. Specifically, in this work, through rigorously modeling the training process and deriving an upper bound on FEE’s one-round convergence rate, we establish a problem of joint resource allocation and data selection, which, unfortunately, cannot be solved directly. Toward this end, we equivalently transform the original problem into a solvable form via a variable substitution and then break it into two subproblems, that is, the resource allocation problem and the data selection problem. The two subproblems are mixed-integer non-convex and integer non-convex problems, respectively, and achieving their optimal solutions is a challenging task. Based on the matching theory and applying the convex-concave procedure and gradient projection methods, we devise a low-complexity suboptimal algorithm for the two subproblems, respectively. Finally, the superiority of our proposed scheme of joint resource allocation and data selection is validated by numerical results.

Wireless Sensor Networks (WSNs) have emerged as an efficient solution for numerous real-time applications, attributable to their compactness, cost-effectiveness, and ease of deployment. The rapid advancement of 5G technology and mobile edge computing (MEC) in recent years has catalyzed the transition towards large-scale deployment of WSN devices. However, the resulting data proliferation and the dynamics of communication environments introduce new challenges for WSN communication: (1) ensuring robust communication in adverse environments and (2) effectively alleviating bandwidth pressure from massive data transmission. In response to the aforementioned challenges, this paper proposes a semantic communication solution. Specifically, considering the limited computational and storage resources of WSN devices, we propose a flexible Attention-based Adaptive Coding (AAC) module. This module integrates window and channel attention mechanisms, dynamically adjusts semantic information in response to the current channel state, and facilitates adaptation of a single model across various Signal-to-Noise Ratio (SNR) environments. Furthermore, to validate the effectiveness of this approach, the paper introduces an end-to-end Joint Source Channel Coding (JSCC) scheme for image semantic communication, employing the AAC module. Experimental results demonstrate that the proposed scheme surpasses existing deep JSCC schemes across datasets of varying resolutions; furthermore, they validate the efficacy of the proposed AAC module, which is capable of dynamically adjusting critical information according to the current channel state. This enables the model to be trained over a range of SNRs and obtain better results.

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.