6G wireless networks have raised increasing attention with computation-sensitive services such as AI Internet of things (AIoT) and mobile augmented reality/virtual reality (AR/VR) applications. Mobile edge computing (MEC) provides rich computation resources for user equipments (UE) at the edge of networks. Aided by MEC servers, computation-intensive applications that are commonly modeled as Directed Acyclic Graphs (DAG) can be performed locally and offloaded to MEC servers to enhance execution efficiency. However, it is a key issue to efficiently provide low latency with limited energy. In this paper, we investigate a multiobjective task scheduling problem in MEC-aided 6G network. Then, an improved multiobjective cuckoo search (IMOCS) algorithm is proposed to deal with a DAG-based task scheduling problem, which aims to reduce the execution latency and energy consumption of UE. Particularly, the proposed IMOCS algorithm is based on the single-objective cuckoo search algorithm and Pareto dominance. An external archive is used to record nondominated solutions, whose update strategy improves the quality of solutions by the aid of fast nondominated sorting and crowding distance sorting. Simulation results demonstrate that IMOCS algorithm outperforms other four benchmark algorithms, which can provide optimal task scheduling policy for MEC severs in 6G networks.

Cooperative formation control is the research basis for various tasks in the multi-UAV network. However, in a complex environment with different interference sources and obstacles, it is difficult for multiple UAVs to maintain their connectivity while avoiding obstacles. In this paper, a Connectivity-Maintenance UAV Formation Control (CMUFC) algorithm is proposed to help multi-UAV networks maintain their communication connectivity by changing the formation topology adaptively under interference and reconstructing the broken communication topology of a multi-UAV network. Furthermore, through the speed-based artificial potential field (SAPF), this algorithm helps the multi-UAV formation to avoid various obstacles. Simulation results verify that the CMUFC algorithm is capable of forming, maintaining, and reconstructing multi-UAV formation in complex environments.

Recently, mobile edge computing (MEC) has been recognized as an emerging paradigm to meet the ever-increasing computation demands. Besides, multiple radio access technology (multi-RAT) has been proposed to enhance the network throughput and service reliability. However, the two technologies have been evolving independently. In this paper, we will develop an integrated multi-RAT and MEC network framework, which enables smart devices (SDs) to offload computation tasks over multiple RAT links in parallel. Considering the resource-limited nature as well as the time-varying property of the proposed network, we then focus on investigating long-term fairness guarantee mechanisms to facilitate fair resource sharing/allocation between SDs. Specifically, we formulate a max-min stochastic optimization problem with the objective of maximizing the minimum long-term time-average offloading utility. An adaptive task splitting and resource allocation algorithm is proposed based on the Lyapunov optimization technology, which jointly optimizes the SD task splitting and uplink transmit power allocation, the RAT subcarrier allocation, and the MEC computation frequency allocation. The adaptive algorithm can accommodate to the time-varying network dynamics without requiring their distribution information. Moreover, the proposed algorithm is shown to have polynomial computation complexity and be asymptotically optimal by rigorous analysis. Simulation results verify the theoretical analysis and show that the Jain's fairness index of the proposed algorithm converges to 1 as the number of time-slots grows, which outperforms the benchmark algorithms.

With the rapid development of Unmanned Aerial Vehicle (UAV) technology, UAV swarms are used for emergency tasks in various scenarios such as area detection, fire rescue, logistics, and transportation. However, for complex scenarios, UAV swarms are prone to environmental interference that damages their equipment or disrupts their communication links, affecting the normal execution of tasks. In this paper, an information–communication interdependent network model is designed for the vulnerability analysis of UAV swarm networks with emergency tasks. Firstly, from the perspective of network functions of a UAV swarm, we introduce the theory of interdependent networks to abstract the relationship between the UAV swarm’s communication network and its information network, where the communication network represents its communication topology and the information network is related to the function of each UAV individual in the UAV swarm. Then, the vulnerability of the UAV swarm is analyzed according to the relationship between network construction costs and network connectivity under environmental interference. Finally, the effectiveness of the vulnerability analysis method is verified through simulation.

To address the resource allocation problem in dynamic environments where multiple unmanned aerial vehicle base stations (UAV-BSs) provide efficient downlink services to ground users, this paper proposes a novel hierarchical decision-making mechanism based on the Proximal Policy Optimization (PPO) algorithm. The proposed method optimizes time-frequency resource allocation in the downlink, aiming to maximize the total user throughput over multiple time slots. By constructing channel and interference models, the complex multi-channel resource allocation problem is decomposed into a series of single-channel decision subproblems, significantly reducing the action space complexity. Specifically, the original exponential complexity $\mathcal{O}\left(N^M\right)$ (where N is the number of users and M is the number of channels) is reduced to a linear complexity $\mathcal{O}\left(N\right)$, effectively alleviating the curse of dimensionality. Simulation results demonstrate that the proposed hierarchical architecture, integrated with the PPO algorithm, achieves superior performance in terms of total throughput, convergence speed, and stability compared to existing methods. This study provides new insights and technical support for efficient resource management in UAV-BS systems operating in complex and dynamic environments.