Memory-Native Non-Terrestrial Networks for Embodied Intelligence

2026-06-22

AI Summary

该研究针对非地面网络中动态、资源受限等问题，提出一种基于记忆的非地面网络（Mem-NTN）方案，通过双存储架构优化决策过程。

Abstract

Non-terrestrial networks (NTN) provide ubiquitous connectivity for embodied intelligence (EI), enabling robots in wilderness to leverage cloud resources or report critical information to remote centers. However, the synergy is nontrivial due to the highly-dynamic, resource-constrained, topology-varying, and task-oriented environment. Existing memoryless NTN protocols become inefficient, since the decisions are driven by local channel conditions and instantaneous service demands. To address these limitations, this paper proposes the memory-native NTN (MemNTN) paradigm that leverages long-horizon contexts for memory augmented system optimization. To realize this paradigm shift, we establish a dual-memory architecture that distinguishes between physical memory representing the state of the world and digital memory encoding historical network experience. We develop memory acquisition, compression, valuation, update, and utilization mechanisms that facilitate cross-layer, memory-native decision-making, spanning from the physical and access layers up to the network and application layers. Experiments in satellite embodied question answering (SEQA) demonstrate that the proposed MemNTN significantly outperforms conventional stateless NTN and terrestrial approaches.

Problem

• Non-terrestrial networks
• Embodied intelligence
• Dynamic environment
• Resource-constrained
• Topology-varying

Innovation

• memory-native NTN
• dual-memory architecture
• long-horizon contexts
• cross-layer decision-making
• satellite embodied question answering