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Nei Kato

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Review 2026

Space Computing Power Networks: A Survey

Space Computing Power Networks (SCPNs), also termed as Satellite Comptuting Power Networks, as an integration of satellite networks, orbital computing, and terrestrial infrastructure, have been becoming an emerging architecture and attracting growing research attention during the past few years. Beyond meeting the differentiated intelligent communication, computing, and caching service requirements from users and terminals across space, air, ground, and sea, SCPNs hold significant importance for space exploration, earth observation, environment monitoring, remote user activities, and so on. There is no doubt that SCPNs will be the critical part of 6G to realize the ubiquitous and seamless intelligence. However, compared to traditional Terrestrial Computing Power Networks (TCPNs) and Satellite Computing Networks (SCNs), SCPNs holds the uniqueness, such as the cycled node movements, hierarchical network topology, extremely large network scalability, ubiquitous resource heterogeneity and constraints, and particular space computing environment. The system integration, protocol optimization, service orchestration, and sustainable operation of SCPN have inspired many meaningful research and projects. Considering existing survey papers mainly focus on scenarios of TCPNs carrying large-scale and complex computing tasks, this paper presents a comprehensive survey of state-of-the-art research on SCPN, covering various aspects ranging from system architecture, applications and challenges, and diversified Quality of Service (QoS) metric analysis and optimization. Finally, a number of potential future research directions have also been discussed to enlighten more innovative works.

Shi Chen, Y. Wu, Bomin Mao et al. · 0 citations
2026

Collaborative Trajectory and Resource Optimization in Multi-UAV MEC Under Jamming: An LLM-Guided MARL Framework

Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) systems provide flexible computing services for resource-constrained devices, but malicious jamming attacks introduce dynamic channel conditions and resource competition, making joint trajectory and resource optimization challenging. This paper investigates this problem in multi-UAV MEC systems under jamming, aiming to minimize delay and energy consumption while ensuring anti-jamming robustness. The problem is formulated as a decentralized partially observable Markov decision process (Dec-POMDP). However, traditional multi-agent reinforcement learning (MARL) approaches struggle with high exploration costs and low sampling efficiency in high-dimensional hybrid action spaces. To overcome these limitations, we propose an LLM-guided MARL framework instantiated with the multi-agent deep deterministic policy gradient (MADDPG), which leverages LLM-generated semantic trajectory prompts to dynamically constrain exploration within the continuous action space, effectively compressing the policy search space and accelerating convergence. Simulation results demonstrate that the proposed method achieves $3.4\times $ to $5\times $ faster convergence over hierarchical MADDPG, MADDPG, and independent soft actor-critic (ISAC) baselines, significantly reducing training costs while maintaining superior performance and anti-jamming robustness.

Yeguang Qin, Jie Tang, Fengxiao Tang et al. · 0 citations