6G天地一体通感算智能协同网络资源管理技术综述

武燕燕; 吴松; 邓伟

doi:10.11999/JEIT250140

6G天地一体通感算智能协同网络资源管理技术综述

doi: 10.11999/JEIT250140 cstr: 32379.14.JEIT250140

中国移动通信有限公司研究院北京 100053

详细信息

作者简介:
武燕燕：女，研究员，研究方向为空天地一体化网络、星地融合通信中的资源管理等

吴松：男，高工，研究方向为移动通信网络、星地融合网络等

邓伟：男，所长、正高级工程师，研究方向为4G、5G 行业网技术及应用、5G- Advanced/6G 天地一体化技术等

通讯作者:
吴松　wusong@chinamobile.com

中图分类号: TN929.5
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出版历程
- 收稿日期: 2025-03-10
- 修回日期: 2025-07-17
- 网络出版日期: 2025-07-24
- 刊出日期: 2025-08-27

An Overview of Resource Management Technology of 6G Integrated Communication, Sensing, and Computation Enabled Satellite-Terrestrial Intelligent Network

China Mobile Research Institute, Beijing 100053, China

摘要

摘要: 针对6G天地一体通感算智能协同网络(6G Integrated Communication, Sensing, and Computation Enabled Satellite-Terrestrial Intelligent Network, 6G-ICSC-STIN)，该文在总结其研究现状的基础上，阐述了未来天地一体通感算智能协同网络的关键技术，分析了频谱共享技术、高精度感知算法、动态计算资源调度以及人工智能(Artificial Intelligence, AI)技术等四大关键领域的研究进展，并讨论了通感算融合衡量指标，提出了6G-ICSC-STIN架构。为满足未来通信网络对高带宽、低时延、广覆盖的多元化需求，设计了高效分布式智能资源管理策略，并在此策略的基础上进一步提出了基于博弈论-多智能体强化学习的资源管理架构。最后，基于跨域技术融合创新以及资源融合表征等未来重点研究方向进行了讨论与展望。
- 6G /
- 网络架构 /
- 天地一体通感算智能协同网络 /
- 资源管理
Abstract: Significance As 6G mobile communication systems continue to evolve, Integrated Communication, Sensing, and Computation (ICSC) technology has emerged as a key area of research. ICSC not only improves network performance but also meets increasingly diverse and personalized user requirements. Recent progress in spectrum sharing, high-precision sensing algorithms, dynamic computing resource scheduling, and Artificial Intelligence (AI) has supported the development of 6G networks. However, several challenges remain. These include inefficient spectrum utilization, limited accuracy and real-time performance of sensing algorithms, and insufficient adaptability and intelligence in computing resource scheduling strategies. Moreover, integrating these technologies into the 6G ICSC Enabled Satellite-Terrestrial Intelligent Network (6G-ICSC-STIN) for effective resource management and optimal allocation is an unresolved issue. To address demands for high bandwidth, low latency, and wide coverage in future networks, a distributed intelligent resource management strategy is designed. Based on this approach, a resource management framework combining game theory and multi-agent reinforcement learning is proposed, offering guidance for advancing resource management in 6G-ICSC-STIN systems. Progress This paper provides a comprehensive discussion of resource management technologies for 6G ICSC Enabled Satellite–Terrestrial Intelligent Networks (6G-ICSC-STIN). It summarizes key technological advances driving the field and presents recent progress in four core areas: spectrum sharing, high-precision sensing algorithms, dynamic computing resource scheduling, and the application of AI in ICSC systems. Measurement indicators for ICSC performance are also examined. Based on this review, a 6G-ICSC-STIN architecture is proposed (Fig. 2), integrating 6G communication, sensing, computation, and intelligent coordination technologies. This architecture fully leverages the capabilities of satellites, unmanned aerial vehicles, High-Altitude Platforms (HAPs), and ground terminals to enable seamless and full-domain coverage across space, air, ground, and sea. It supports deep integration of communication, sensing, computation, intelligence, and security, resulting in a unified network system characterized by more precise perception and transmission, improved resource coordination, lower system overhead, and enhanced user experience. To address complex resource management challenges, a functional block diagram comprising the application, service, capability, and resource layers is introduced (Fig. 3), aiming to identify new approaches for efficient resource allocation. A distributed intelligent resource management strategy is further proposed for the ICSC central, fog node, edge networks and terminal (Fig. 4). Within the integrated edge network, a novel “Master–Slave two-level edge node” architecture is designed, in which the Master node deploys a resource demand prediction model to estimate regional demand in real time (Fig. 6). Building on this strategy, a resource management framework based on game theory and multi-agent reinforcement learning is proposed (Fig. 5). This framework employs the Nash-Equilibrium Asynchronous Advantage Actor-Critic (Nash-E-A3C) algorithm, adopts a parallelized multi-agent and distributed computing approach, and integrates Nash equilibrium theory (Fig. 7), with the aim of achieving intelligent, collaborative, and efficient network resource management. Conclusions The distributed intelligent resource management strategy is essential for achieving efficient resource coordination and optimal utilization in the 6G-ICSC-STIN architecture. By decentralizing computing, storage, and communication resources across network nodes, it enables resource sharing and collaborative operation. The proposed architecture, grounded in game theory and multi-agent reinforcement learning, supports dynamic resource allocation and optimization. Agents are deployed at each node, where they make decisions based on local demands and environmental conditions using game-theoretic reasoning and Reinforcement Learning (RL) algorithms. This approach enables globally efficient resource management across the network. Prospects Cross-domain technological integration is fundamental to the realization of 6G-ICSC-STIN. Deep integration of sensing, communication, and computing capabilities can substantially enhance overall network performance and efficiency. However, this integration faces several challenges, including heterogeneous network compatibility, complex resource scheduling, fragmented security mechanisms, and slow progress in standardization. Efficient resource representation is critical for effective resource management and performance optimization. Existing studies show that resources in satellite-terrestrial integrated networks are heterogeneous, multidimensional, and unevenly distributed across large spatiotemporal scales, posing new challenges to resource coordination. This paper outlines future development trends in intelligent resource management for 6G-ICSC-STIN, synthesizing current research progress, key challenges, and future directions in cross-domain technology fusion and resource representation. These emerging technologies together form a foundation for intelligent and efficient resource management in 6G-ICSC-STIN and offer new pathways for the advancement of next-generation wireless communication systems.
- 6G /
- Network architecture /
- Integrated communication, sensing, and computation enabled satellite-terrestrial network /
- Resource management

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图 1 频谱共享技术分类

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图 2 6G-ICSC-STIN架构

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图 3 6G-ICSC-STIN功能框图

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图 4 6G-ICSC-STIN分布式智能资源管理

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图 5 6G通感算一体化星地智能协同资源管理架构图

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图 6 ICSC边缘Master节点上部署的资源需求预测模型基本流程

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图 7 基于Nash-E-A3C算法的6G天地一体化通感算智能协同资源管理算法流程图

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表 1 频谱共享技术研究现状

频谱共享架构	优化目标	频谱共享算法	优化资源维度	代表文献
星间频谱共享架构	提升频谱利用率与系统吞吐量	最优化理论	多维度联合优化(时间、功率、空间)	[5]
星地频谱共享(高轨卫星(High Elliptical Orbit, HEO)、低轨卫星(Low Earth Orbit, LEO)、地面基站、卫星及基站各自用户)	频谱资源高效利用以及系统SINR更高	DRL算法	联合优化多域(卫星域、波束域、功率域、频谱域)	[4]
星地一体化频谱共享	最大化加权和速率(Weighted Sum Rate, WSR)	最优化理论	功率	[25]
星地一体化频谱共享	最大化最小速率(Max-Min Rate, MMR)	最优化理论(基于WMMSE(Weighted Minimum Mean Square Error)的改进交替优化(AO)算法)	功率	[27]
星地频谱共享架构	用户收益最大化、公平分配、系统效用提升	Stackelberg博弈及双目标WOA	以频率为主，空间为辅	[21]
星地频谱共享(LEO、地面基站、卫星及基站各自用户)	提升LEO网络的服务满意度和频谱效率	最优化理论(李雅普诺夫框架(Lyapunov框架))	时间、空间	[28]
星地频谱共享(一对非静止轨道(NGSO) 星座和多个地面基站)	增强频谱利用效率	利用AI和LLMs来检测干扰和优化频谱共享	时间、空间、频率	[26]

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表 2 通信、感知、计算的核心性能指标

	核心性能指标1	核心性能指标2	核心性能指标3	代表文献
通信	效率指标：包括峰值速率、频谱效率(单位带宽传输能力)、能量效率(单位能耗传输比特数)，反映资源利用效率	可靠性指标：如BER、中断概率(通信链路稳定性)、传输成功率，直接影响数据完整性	网络化指标：包括覆盖范围、多用户接入能力、网络协作效率(如基站间协同调度)	[56]
感知	可靠性指标： (1)检测可靠性：检测概率(发现目标的概率)、虚警概率(误报率) (2)估计可靠性：距离/角度/速度的MSE、克拉美罗界(理论精度下限) (3)识别可靠性：目标分类准确率、环境重构分辨率	效率指标：单位资源下的感知范围、目标数量上限、感知更新频率	网络化指标：多节点协作感知能力、移动目标跟踪连续性、感知资源共享效率	[56,57]
计算	处理能力：算力密度(单位时间计算量)、任务处理延迟(端到端响应时间)	资源利用率：计算负载均衡度、分布式任务调度效率	智能水平：算法复杂度、模型推理精度(如AI模型的准确率)	[58]

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表 3 典型应用场景的指标需求、技术挑战及解决方案

场景	核心指标	技术挑战	解决方案示例
智慧交通	定位精度(≤10 cm)、时延(≤5 ms)、多目标跟踪密度(>1000个/km²)^[63]	高速移动场景下的信号多普勒效应、计算资源动态分配	动态波束成形及AI信道预测^[7]
智能制造	设备状态识别准确率(≥99%)、云化控制延迟(≤20 ms)、抗干扰能力^[62]	工业环境电磁干扰、异构设备协议兼容性	轻量级协议转换中间件^[64]
智慧城市	环境监测覆盖率(≥95%)、数据融合效率(TB级/小时)、能耗效率(W/bit)	海量异构数据实时处理、长期运行的系统稳定性	利用DL算法，结合注意力机制、多任务学习和正则化技术，设计模型以适应数据的多样性和实时性需求^[65]
无人机应用	低空感知分辨率(≤0.1 m²)、通信-计算协同时延(≤50 ms)、续航优化	弱反射信号处理、机载算力受限	调整资源分配以实现任务卸载和本地计算的均衡，同时考虑公平性^[66]
DT	模型更新频率(≥3 0Hz)、多源数据一致性(误差<1%)、虚实同步精度	高维数据实时渲染、跨平台数据标准化	借助云端渲染引擎，利用分布式GPU集群进行高性能计算，降低对终端设备的依赖^[67]

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表 4 网络资源表征主要方法特点及研究现状

	通信资源	存储资源	具有单一计算功能的计算资源	具有多个计算功能的计算资源	优点	缺点	代表文献
SSG	$ \surd $	–	$ \surd $	–	通过离散的快照序列刻画网络拓扑的动态变化，适用于基于静态图设计的路由算法。简单直观，易于理解和实现。	信息丢失：每个快照仅保留特定时间点的状态，忽略快照内部的时序交互细节。存储开销大：随着时间片数量增加，快照的存储成本显著上升。资源表征不完整：忽略了存储资源，且无法表征节点的多计算功能。	[84]
TEG	$ \surd $	$ \surd $	$ \surd $	–	时空联合建模：通过存储链路连接快照，同时表征通信和存储资源，提高资源利用率和网络性能。高表征精度：能够精确描述网络拓扑的时序演进过程，适用于需要精细化建模的场景。	计算与存储复杂度高：节点和链路的复制导致模型规模膨胀，尤其在大规模网络和长周期场景下，路由计算复杂度极高。无法表征计算资源：仅关注通信和存储资源，未包含节点的多计算功能。	[85]
存储时间聚合图	$ \surd $	$ \surd $	–	–	精简模型：通过聚合TEG减少冗余信息，降低空间复杂度。路由算法高效：一次计算可得出TEG中的多条路径，适合需要快速决策的场景。	信息简化：聚合过程可能丢失部分时序细节，表征精度低于TEG。仍缺乏计算资源表征：与TEG类似，未涵盖节点的计算能力。	[86]
多功能时间扩展图	$ \surd $	$ \surd $	$ \surd $	$ \surd $	全面资源表征：同时建模通信、存储和计算资源，适用于软件定义的复杂网络 (如星地融合网络)。灵活性与适应性：支持动态资源分配和功能调整，适合多任务协同场景。	极高的时间复杂度：在网络规模大或时间周期长时，优化问题的求解复杂度呈指数级增长，实际应用受限。实现复杂：需设计低复杂度算法以平衡性能与效率，目前仍是研究难点。	[84]

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