In this paper, we investigate a movable antenna (MA)-assisted uncrewed aerial vehicle (UAV) swarm communication system. Unlike conventional fixed-position antenna (FPA) systems, each UAV is equipped with an MA array distributed on two hemispherical surfaces at the head and tail, significantly expanding the spatial degrees of freedom (DoFs) in three-dimensional (3-D) seamless coverage. A far-field line-of-sight (LoS) channel model is adopted to characterize the UAV-to-UAV (U2U) communication links, incorporating both antenna positioning and radiation patterns. We formulate an achievable sum rate maximization problem by jointly optimizing the antenna position vectors (APVs) and transmit/receive beamforming vectors, subject to constraints on maximum transmit power, limited antenna moving region, and minimum inter-antenna spacing. To tackle this non-convex and highly coupled problem, we propose a two-loop iterative optimization algorithm that effectively combines the Spider Wasp Optimizer (SWO) for APV optimization and alternative optimization (AO) for beamforming design. Extensive simulation results demonstrate that the proposed MA-assisted scheme outperforms traditional FPA systems and other benchmark algorithms under various settings. The performance gains are attributed to the efficient optimization of antenna positions within the hemispherical moving region for interference suppression and coverage enhancement.
Fansheng Song, Lipeng Zhu, Xiangyu Pi et al.· IEEE Transactions on Communi...· 0 citations
With the rapid development of artificial intelligence and low-earth orbit (LEO) satellite edge computing technology, there has been a rapid increase in the demand for intelligence networks and services among users in remote areas, such as federated learning (FL). We propose a satellite aided computation FL (SACFL) system in LEO ubiquitous edge computing (UEC) networks, aiming at improving the efficiency of FL tasks in remote areas. In the considered deployment scenario, the following key factors are considered: 1) terrestrial users in remote areas; 2) offloading data to satellites for aided computation; and 3) global aggregation on satellite. However, the highly dynamic characteristics and the uneven distribution of satellite computation resources pose significant challenges to the low-delay requirements of satellite-based FL tasks. To this end, we formulate an optimization problem to minimize delay by jointly considering access selection, computation offloading, aggregation satellite (AgS) selection, and computation resource allocation. To solve the formulated problem, we propose a novel multi-agent alternating (M2A) optimization method. Specifically, three independent agents are trained alternately to make decisions on access selection, computation offloading, and AgS selection. Comprehensive simulations demonstrate that the proposed method outperforms other benchmark algorithms in terms of convergence, delay, and FL accuracy.
Junyi Yang, Yafeng Ma, Zhenyu Xiao et al.· IEEE Transactions on Cogniti...· 0 citations