Search

Unmanned aerial vehicle (UAV) has been utilized as an efficient data collector for Internet of Things (IoT) networks in sustainable industry 5.0. Whereas, how to sustain a stable power for the energy-constrained IoT devices (IoTDs) and to enhance the data gathering throughput of UAV-aided IoT networks via the wireless power transfer (WPT) or non-orthogonal multiple access (NOMA) is a twofold challenge. Thus, we propose to maximize the minimum UAV data collection throughput from the IoTDs via jointly optimizing the three-dimensional (3D) trajectories of two UAVs, scheduling and transmitting power of the IoTDs subject to the maximum flight velocity and minimum safe distance for the UAVs, as well as the harvested energy causality constraint for each IoTD during a finite UAV flight mission period. To tackle this non-convex problem with the strong interdependence of optimization parameters, we develop a 3D Trajectory Optimization and communication Resources Allocation Algorithm, named as TORAA, via employing the alternating optimization and successive convex approximation approaches, which alternately optimizes the UAVs’ 3D trajectories, the IoTDs’ scheduling and transmitting power sub-problems until the convergence criterion is met by the target function value. Moreover, we analyze the complexity and convergence characteristic of the TORAA. Numerous simulations have been performed to validate that the TORAA is capable of noteworthily enhancing the maximum minimum data collection throughput compared to the benchmark schemes with two-dimensional (2D) trajectories of the UAVs or constant transmitting power of the IoTDs.

Efficient image transmission while preserving semantic information is crucial for many applications but poses challenges when communication channels have limited capacity. This letter presents an end-to-end semantic communication system for image transmission by exploiting semantic information, which is different from traditional approaches that emphasize pixel-level information. Specifically, identical semantic knowledge libraries are shared between the sender and receiver to associate image contents with semantics. At the sender, a deep learning-based classifier categorizes the image, and a dictionary learning method extracts features. At the receiver, a modified diffusion model-based generator reconstructs the image from the received features and category, with the objective to minimize the reconstruction error. To evaluate the semantic fidelity, we propose a semantic fidelity index (SFI) that considers both mutual information and neural network (NN) feature similarity between the original and reconstructed images. Experiments demonstrate that, by leveraging the shared semantic prior knowledge base, our approach can efficiently convey image semantics and achieve high-quality reconstruction. The proposed system provides an effective solution for semantic-preserving image communication in bandwidth-limited applications.

Federated edge learning (FEEL) emerges as a privacy-preserving paradigm to effectively integrate edge computing for the implementation of deep learning-based vehicular applications. Nevertheless, the incentive mechanism for vehicles participating in varied learning tasks, has not been well explored yet. In this paper, software-defined network (SDN) technology is adopted for the training control among vehicles, and a novel FEEL framework, namely SDN-assisted semi-decentralized FEEL (SSD-FEEL) is investigated, where multiple edge servers collectively coordinate a large number of vehicular models from different learning tasks. By exploiting the low-cost and similar learning tasks among vehicles and edge servers, SSD-FEEL incorporates more training samples, while enjoying the flexibility of edge server assisted model aggregation. Aiming at motivating vehicles to actively participate in training while improving the model accuracy of multiple learning tasks, a joint contract design and task reorganization problem, combined with the evaluation of model convergence and contract performance, is formulated. Then, we propose a two-stage optimization algorithm incorporating iterative reward allocation and task matching, where the model parameters in different tasks are reconstructed according to the matching results with the mobility constraints. Extensive experiments conducted on multiple data sets validate that the proposed algorithm can achieve higher cluster utility and outperform the conventional multi-task FEEL schemes in terms of learning performance.

In current 6G study, space-aerial-ground integrated networks (SAGIN) are attracting the eye-catching attention. Traditional ground networks and facilities (e.g., open random access networks, Open-RAN) simply provide two-dimensional (2D) and limited-coverage services at low altitudes. SAGIN are designed to overcome these limitations and promise to provide 3D and seamless services and connectivity opportunities. Meanwhile, network softwarization (NetSoft) is highlighted as dominant 6G attribute. Through incorporating NetSoft, dedicated network appliance will be isolated as software blocks and general-purpose hardware. Initiated and customized services will be realized by chained software blocks and share the same underlying hardware. Thus, the resource usage and service diversity can be simultaneously achieved by adopting NetSoft. In current literature, SAGIN and NetSoft are well investigated. However, their joint research is at early stage. Therefore, we investigate the resource allocation in softwarized SAGIN for achieving open and seamless access services. We propose the efficient resource allocation and orchestration framework, abbreviated as Slice-Soft-SAGIN. Our framework approach is designed to achieve efficient resource allocation solution per slice service. Each time receiving one tailored slicing service, the first checking available resources will be conducted by our Slice-Soft-SAGIN. When passing the preliminary resource checking, the formal resource allocation of slicing service will be executed in order: first terrestrial node, second aerial node, third satellite node. In addition, our Slice-Soft-SAGIN executes wireless (spectrum) and wired (computing and storage) types of resources when serving this slicing service. This is one crucial highlight of our Slice-Soft-SAGIN framework. With the purpose of confirming merits of Slice-Soft-SAGIN, the comprehensive evaluation work is executed. Typical works and three extra counterparts constitute the whole evaluation. We analyze the evaluation results carefully.

How to improve the coverage capability of network including connectivity and throughput is vital for enabling the Internet of Everything (IoE) in B5G/6G. However, the traditional terrestrial networks are confronted with the high-cost and inflexible challenges especially in the remote area and emergency applications. In order to solve these challenges, we consider a heterogeneous aerial network (HetAN), where some low-altitude base stations (LBSs) are deployed as access points for wireless coverage and a high-altitude base station (HBS) hovers as the hub for backhaul of LBSs. Furthermore, we apply the non-orthogonal multiple access (NOMA) to uplink transmission for the terrestrial users, which enable massive connectivity in the IoE. To maximize connectivity and throughput, we jointly optimize the LBSs’ deployment, power control, channel allocation, and rate control by fully exploiting the potential of the HetAN in wide-area coverage. For solving the formulated problem efficiently, we propose an iterative algorithm based on the methods of graph theory, bionic algorithm, and theoretical analysis. Simulation results are provided to reveal the influence of the control variables on network performance and indicate that our algorithm can greatly improve the connectivity and throughput with the other schemes.