The capacity and reliability of low Earth orbit (LEO) mega constellations is restricted by traditional point-to-point relaying due to resource constraints and frequent link interruptions. To address these challenges, we propose a multi-satellite coordinated relaying (MSCR) scheme. The ground station determines when to sample for resilient multi-link broadcasting via the introduced utility of information (UoI). To combat error-and interference-prone downlink, we design a dual-layer error control (DEC) mechanism integrating intra-packet physical-layer network coding (PNC) with inter-packet long erasure coding (LEC). Moreover, the relay satellites adaptively switch between diversity and multiplexing modes based on channel state to further ensure reliability. To maximize the UoI of MSCR, we first derive the block error rate (BLER) bounds under varying policies, and then formulate a joint scheduling problem encompassing the sampling decision at the source, the relaying modes and power allocation at the relays, and the number of LEC packets at both ends. A Lyapunov-guided game-theoretic alternating iterative (GAI) approach is proposed to solve this problem. Simulation results underscore the superiority of our MSCR scheme over state-ofthe-art benchmarks in terms of UoI, delay and throughput.
Jian-Hao Huang, J. Jiao, Qing-Xi Liu et al.· 2026 IEEE/CIC International...· 0 citations
The rapid evolution of swarm intelligence and edge computing has highlighted the potential of Uncrewed Aerial Vehicle (UAV) swarms for data-driven services. However, heterogeneous capabilities and time-varying communication conditions pose significant challenges for efficient resource orchestration. This letter proposes a novel method for joint communication topology formation and computation offloading in heterogeneous UAV networks to optimize task completion time and energy consumption. A graph attention network is employed for swarm feature extraction, and the communication topology and task offloading ratios are determined by proximal policy optimization. Successive convex approximation is further applied for bandwidth and power allocation. Simulation results demonstrate that the proposed framework effectively reduces task completion time and energy consumption compared with other benchmarks.