Unmanned aerial vehicles (UAVs) have emerged as effective platforms for mobile edge computing (MEC), offering flexible and efficient computational support to ground users (GUs). Many practical applications, such as deep neural network inference tasks, generate subtasks with complex dependencies, significantly complicating scheduling and offloading decisions. In this paper, we study the joint optimization of UAV deployment, UAV-GU associations, and dependent task offloading decisions within a multi-UAV-enabled MECsystem, aiming to minimize the end time of the overall tasks. The tasks generated by GUs are modeled using directed acyclic graphs (DAGs), explicitly capturing subtask dependencies and execution orders. To address the resulting complex optimization problem, we first propose a Joint Successive convex approximation and Penalty dual decomposition-based Optimization (JSPO) algorithm to determine the initial UAV deployment and UAV-GU associations. Next, we formulate the dependent task offloading decision process as a Markov decision process (MDP), which is solved by employing deep reinforcement learning (DRL). To effectively exploit the structural information within DAG tasks, we integrate a graph attention network (GAT) to provide enhanced state representations for DRL. JSPO and the DRL framework were executed in turns to gradually improve the performance. Extensive simulation results verify that our proposed framework significantly reduces the end time compared to existing methods, demonstrating its superiority in multi-UAV MEC systems.

This paper studies the unmanned aerial vehicle (UAV)-enabled aerial monitoring assisted by simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs), in which one UAV aims to monitor a number of moving targets, and one STAR-RIS is installed on a building for assisting the UAV to broadcast the monitored information to both indoor and outdoor users. Due to the randomness of target movements over time, the UAV needs to adaptively adjust its flight trajectory to track them. This thus results in highly dynamic channel conditions and uncertain UAV energy consumption, which accordingly make the efficient aerial monitoring a challenging task. To address these challenges, we propose a STAR-RIS-aided UAV-enabled aerial monitoring framework, which aims to maximize the long-term average throughput for all users, through joint optimization of transmit beamforming, UAV trajectory, and STAR-RIS configuration, while ensuring the monitoring requirements under strict energy constraints. The formulated problem is a multi-stage stochastic optimization problem, due to the randomness of various system parameters. To handle this problem, we apply the Lyapunov optimization technique and introduce a virtual energy queue to transform it into a series of single-slot optimization subproblems that are solvable online. For each subproblem, we develop efficient algorithms to obtain a near-optimal solution, in which a penalty dual decomposition (PDD) approach is used for the transmit beamforming and STAR-RIS configuration optimization, and a sequential parametric convex approximation (SPCA) method is used for UAV trajectory optimization. Extensive simulations demonstrate that the proposed framework significantly outperforms benchmark schemes, effectively maximizing the throughput and energy efficiency under dynamic operational conditions.

Semantic communication is a novel communication paradigm that demonstrates great potential in information transmission applications, particularly in challenging scenarios characterized by low signal-to-noise ratio (SNR) conditions. It extracts task-relevant semantic information and performs end-to-end optimization of source and channel coding. However, current mainstream architectures do not account for the importance of features in the subsequent reconstruction process. This shortcoming leads to the transmission of all features, resulting in inefficient bandwidth utilization. Furthermore, existing methods reconstruct all images equally, regardless of their differences in complexity, which is inefficient and wastes computational resources. To address these issues, we develop an efficient semantic communication architecture, termed ESC. Specifically, we design feature selection and reconstruction modules to filter out unimportant information, addressing the problem of transmission feature redundancy and improving bandwidth utilization efficiency. In addition, we develop a multi-branch decoder architecture and an adaptive inference strategy to accommodate the varying complexities of images, allowing samples with satisfactory reconstruction results to exit the decoder network early, thus reducing inference costs. By introducing feature selection, our architecture’s reconstruction quality surpasses that of 5G systems and mainstream semantic communication under the same bandwidth on the Kodak and CLIC2021 datasets. Our adaptive inference strategy achieves speed-ups of approximately 1.37 × and 1.43 × respectively, with only minimal degradation in image reconstruction quality.

Semantic communication is an emerging paradigm with significant potential for image transmission. However, resource-efficient architecture design and resource allocation in this field have not received adequate research attention. This paper proposes a resource-efficient multi-branch semantic communication architecture based on saliency detection, aimed at optimizing computational efficiency in image transmission. The architecture leverages models with varying capacities to process regions of images with different complexities. We further address the problem of multi-user uplink semantic communication and resource allocation, focusing on minimizing the total energy consumption for communication and computation. The optimization problem, subject to user demand, computation, delay, and transmission power constraints, is non-convex due to the coupling of variables, making it challenging to solve. To tackle this, we introduce a two-level decomposition approach. The lower-level problem, given a fixed compression rate, is solved using Karush-Kuhn-Tucker (KKT) conditions to derive closed-form solutions for transmission power and computation frequency. The upper-level problem, which optimizes the compression rate, is reformulated as a monotone optimization problem for efficient solution finding. Numerical results demonstrate that the proposed architecture significantly reduces computational resource usage while maintaining image quality, and the resource allocation strategy effectively minimizes energy consumption, outperforming baseline schemes in terms of energy efficiency.

Low Earth Orbit (LEO) satellites play a crucial role in providing high-speed internet to remote areas and ensuring network resilience during outages. The design of efficient satellite constellations requires optimizing network topology, which is a complex task due to the large solution space and the need for fault tolerance. This paper presents the AlphaSat algorithm, a two-phase approach to improve latency and network robustness in LEO constellations. In the initialization phase, Monte Carlo Tree Search (MCTS) is used to generate an initial topology by selecting links from a vast search space. In the refinement phase, an edge-switching method is applied to enhance network resilience and performance. AlphaSat is evaluated on OneWeb, Starlink, and Telesat mega-constellations, demonstrating superior performance over existing algorithms. The results show significant reductions in latency ranging from 4.7% to 44.5% and improvements in network robustness, increasing by 3.3% to 28.3%. Furthermore, AlphaSat effectively balances network load and optimizes power consumption, offering a promising solution for efficient and resilient LEO satellite network design.

The integration of edge computing into satellite networks offers a promising solution for extending computational services to remote and underserved areas. To effectively provide a variety of computing services, it is essential to cache the corresponding services on satellites. However, challenges exist such as dynamic computing requests that vary over time and space, energy constraints due to restricted power supply, as well as limited storage capacity on satellites and the impracticality of frequently adjusting service deployments. To tackle such challenges, this paper proposes a two-timescale joint optimization framework to minimize energy consumption in satellite edge computing networks while ensuring the delay requirements, by jointly optimizing service placement and task offloading, as well as computation resource and power allocation. On a larger timescale, we optimize service caching placement by strategically deploying services on satellites and ground devices (GDs) based on long-term service request statistics, aiming to minimize the total average delay over each time frame. We develop an efficient iterative algorithm by employing penalty-based methods and Lagrange duality techniques to achieve suboptimal service deployment. On a smaller timescale, we optimize task offloading and resource allocation in shorter time slots, adapting to dynamic traffic fluctuations to minimize energy consumption while meeting delay constraints. We utilize alternating optimization and quadratic transform methods to efficiently allocate resources and schedule tasks. Extensive simulations demonstrate the effectiveness and superiority of our framework over benchmark schemes, revealing significant reductions in delay and energy consumption. The results also highlight the trade-offs between task delay and energy consumption, as well as between transmit power and energy consumption.