Unmanned aerial vehicles (UAVs) furnished with computational servers enable user equipment (UE) to offload complex computational tasks, thereby addressing the limitations of edge computing in remote or resource-constrained environments. The application of value decomposition algorithms for UAV trajectory planning has drawn considerable research attention. However, existing value decomposition algorithms commonly encounter obstacles in effectively associating local observations with the global state of UAV clusters, which hinders their task-solving capabilities and gives rise to reduced task completion rates and prolonged convergence times. To address these challenges, this paper introduces an innovative multi-agent deep learning framework that conceptualizes multi-UAV trajectory optimization as a decentralized partially observable Markov decision process (Dec-POMDP). This framework integrates the QTRAN algorithm with a large language model (LLM) for efficient region decomposition and employs graph convolutional networks (GCNs) combined with self-attention mechanisms to adeptly manage inter-subregion relationships. The simulation results demonstrate that the proposed method significantly outperforms existing deep reinforcement learning methods, with improvements in convergence speed and task completion rate exceeding 10%. Overall, this framework significantly advances UAV trajectory optimization and enhances the performance of multi-agent systems within UAV-assisted edge computing environments.

An element spacing of less than half a wavelength introduces strong mutual coupling between the ports of compact antenna arrays. The strong coupling causes significant system performance degradation. A decoupling network may compensate for the mutual coupling. Alternatively, port decoupling can be achieved using a modal feed network. In response to an input signal at one of the input ports, this feed network excites the antenna elements in accordance with one of the eigenvectors of the array scattering parameter matrix. In this paper, a novel 4-element monopole array is described. The feed network of the array is implemented as a planar ring-type circuit in stripline with four coupled line sections. The new configuration offers a significant reduction in size, resulting in a very compact array.

Strong mutual coupling between closely spaced elements in compact antenna arrays may cause significant system performance degradation, such as reduction in gain and signal-to-noise ratio. In this p...

The introduction of mobile edge computing (MEC) in vehicular network has been a promising paradigm to improve vehicular services by offloading computation‐intensive tasks to the MEC server. To avoid the overload phenomenon in MEC server, the vast idle resources of parked vehicles can be utilized to effectively relieve the computational burden on the server. Furthermore, unbalanced load allocation may cause larger latency and energy consumption. To solve the problem, the reported works preferred to allocate workload between MEC server and single parked vehicle. In this paper, a multiple parked vehicle‐assisted edge computing (MPVEC) paradigm is first introduced. A joint load balancing and offloading optimization problem is formulated to minimize the system cost under delay constraint. In order to accomplish the offloading tasks, a multiple offloading node selection algorithm is proposed to select several appropriate PVs to collaborate with the MEC server in computing tasks. Furthermore, a workload allocation strategy based on dynamic game is presented to optimize the system performance with jointly considering the workload balance among computing nodes. Numerical results indicate that the offloading strategy in MPVEC scheme can significantly reduce the system cost and load balancing of the system can be achieved.

In order to achieve very high data rates in both the uplink and downlink channels, the multiple antenna systems are used within the mobile terminal as well as the base station of the future generation of mobile networks. When implemented in a size limited platform, the multiple antenna arrays suffer from strong mutual coupling between closely spaced array elements. In this paper, a rigorous procedure for the design of a 4-port compact planar antenna array with high port isolation is presented. The proposed design involves a decoupling network consisting of reactive elements, whose values can be obtained by the method of eigenmode analysis. Numerical results show the effectiveness of the proposed design approach in improving the port isolation of a compact four-element planar array.