AI赋能的通感算一体化关键技术研究综述

朱政宇; 殷梦琳; 姚信威; 徐勇军; 孙钢灿; 徐明亮

doi:10.11999/JEIT250242

AI赋能的通感算一体化关键技术研究综述

doi: 10.11999/JEIT250242 cstr: 32379.14.JEIT250242

1.
郑州大学电气与信息工程学院郑州 450001
2.
浙江工业大学计算机科学与技术学院杭州 310014
3.
重庆邮电大学通信与信息工程学院重庆 400065
4.
郑州大学计算机与人工智能学院郑州 450001

详细信息

作者简介:
朱政宇：男，副教授，研究方向为无线通信和信号处理、5G、物联网、机器学习、大规模MIMO、毫米波通信、无人机通信、物理层安全、无线协作网络、凸优化技术和携能传输等

殷梦琳：女，硕士生，研究方向为通感算一体化技术、深度学习等

姚信威：男，教授，研究方向为群智感知与协同、智联网、智能机器人等

徐勇军：男，教授，研究方向为反向散射通信、UAV通信、异构无线网络等

孙钢灿：男，教授，研究方向为深度学习、机器学习、无线通信、物理层安全技术等

徐明亮：男，教授，研究方向为人工智能、大数据、机器人、工业软件等

通讯作者:
孙钢灿　iegcsun@zzu.edu.cn

中图分类号: TN915.0
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出版历程
- 收稿日期: 2025-04-07
- 修回日期: 2025-06-04
- 网络出版日期: 2025-06-14

Overview of the Research on Key Technologies for AI-powered Integrated Sensing, Communication and Computing

1.
School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China
2.
School of Compute Science and Technology, Zhejiang University Of Technology, Hangzhou 310014, China
3.
School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
4.
School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China

摘要

摘要: 通感算一体化技术与人工智能算法相结合已成为一个非常重要的领域，因其频谱利用率高、硬件成本低等优点，已经成为第6代(6G)网络中的关键技术之一。人工智能(AI)赋能的通感算一体化系统通过集成感知、通信、计算和人工智能功能，可在日益复杂和动态的环境中实现快速数据处理、实时资源优化和智能决策，已经广泛应用于智能车载网络，包括无人机和自动汽车，以及雷达应用、定位和跟踪、波束成形等领域。该文在引入人工智能算法来提高通感算一体化系统性能的基础上，简要介绍了人工智能和通感算一体化的特征与优势，重点讨论了AI赋能的通感算一体化系统的智能网络框架、应用前景、性能指标和关键技术，并在最后对AI赋能的通感算一体化面临的挑战进行了研究展望，未来的6G无线通信网络将超越纯粹的数据传输管道，成为一个集成传感、通信、计算和智能的综合平台，以提供无处不在的人工智能服务。
- 6G /
- 人工智能 /
- 通感算一体化 /
- 深度强化学习
Abstract: The Integration of Sensing, Communication and Computing (ISCC) combined with Artificial Intelligence(AI) algorithms has emerged as a critical enabler of Sixth-Generation (6G) networks due to its high spectral efficiency and low hardware cost. AI-powered ISCC systems, which combine sensing, communication, computing, and intelligent algorithms, support fast data processing, real-time resource allocation, and adaptive decision-making in complex and dynamic environments. These systems are increasingly applied in intelligent vehicular networks—including Unmanned Aerial Vehicles (UAVs) and autonomous driving—as well as in radar, positioning, tracking, and beamforming. This overview outlines the development and advantages of AI-enabled ISCC systems, focusing on performance benefits, application potential, evaluation metrics, and enabling technologies. It concludes by discussing future research directions. Future 6G networks are expected to evolve beyond data transmission to form an integrated platform that unifies sensing, communication, computing, and intelligence, enabling pervasive AI services. Significance AI-powered ISCC marks a transformative shift in wireless communication, enabling more efficient spectrum utilization, reduced hardware cost, and improved adaptability in complex environments. This integration is central to the development of 6G networks, which aim to deliver intelligent and efficient services across applications such as autonomous vehicles, UAVs, and smart cities. The significance of this research lies in its potential to reshape the management and optimization of communication, sensing, and computing resources, advancing the realization of a ubiquitously connected and intelligent infrastructure. Progress Recent advances in AI—particularly in machine learning, deep learning, and reinforcement learning—have substantially improved the performance of ISCC systems. These methods enable real-time data processing, intelligent resource management, and adaptive decision-making, which are critical for future 6G requirements. Notable progress includes AI-driven waveform design, beamforming, channel estimation, and dynamic spectrum allocation, all of which enhance ISCC efficiency and reliability. Additionally, the integration of edge computing and federated learning has mitigated challenges related to latency, data privacy, and scalability, facilitating broader deployment of AI-enabled ISCC systems. Conclusions Research on AI-powered ISCC systems highlights the benefits of integrating AI with sensing, communication, and computing. AI algorithms improve resource efficiency, sensing precision, and real-time adaptability, making ISCC systems well suited for dynamic and complex environments. The adoption of lightweight models and distributed learning has broadened applicability to resource-limited platforms such as drones and IoT sensors. Overall, AI-enabled ISCC systems advance the realization of 6G networks, where sensing, communication, and computing are unified to support intelligent and ubiquitous services. Prospects The advancement of AI-powered ISCC systems depends on addressing key challenges, including data quality, model complexity, security, and real-time performance. Future research should focus on developing robust AI models capable of generalizing across diverse wireless environments. Progress in lightweight AI and edge computing will be critical for deployment in resource-constrained devices. The integration of multi-modal data and the design of secure, privacy-preserving algorithms will be essential to ensure system reliability and safety. As 6G networks evolve, AI-powered ISCC systems are expected to underpin intelligent, efficient, and secure communication infrastructures, reshaping human-technology interaction in the digital era.
- 6G /
- Artificial Intelligence (AI) /
- Integration of Sensing Communication and Computing (ISCC) /
- Deep reinforcement learning

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图 1 6G驱动AI赋能通感算一体化系统

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图 2 人工智能、机器学习、深度学习之间的关系

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图 3 DRL原理图

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图 4 FL原理框架

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图 5 AI赋能通感算一体化网络架构

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图 6 AI赋能通感算一体化应用场景

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表 1 5G与6G部分性能指标对比

性能指标	5G	6G	提升效果
峰值速率	10～20 Gbit/(s·Hz)(理论值)	100 Gbit/(s·Hz) ～1 Tbit/(s·Hz)(理论值)	10～100倍
用户体验速率	0.1～1 Gbit/(s·Hz)	数十Gbit/(s·Hz)	10～100倍
时延	1 ms	10～100 μs	10～100倍
连接密度	10⁶设备/km²	10⁷～10⁸设备/km²	10～100倍
频谱效率	约100 bit/(s·Hz)	150～300 bit/(s·Hz)	1.5～3倍
覆盖范围	地面基站为主	空天地一体化覆盖	全球无缝覆盖

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表 2 AI赋能通感算一体化系统与传统正交频分复用波形系统性能对比

对比维度	AI赋能通感算一体化系统	传统正交频分复用波形系统	关键差异来源
通信性能^[18,19]	AI优化波束成形，误码率降低10%～30% 频谱效率提升	高峰均功率比导致信号失真固定子载波分配效率受限	AI动态优化波形与资源分配
感知精度^[19–21]	MSE降低20%～50% 支持多目标跟踪与语义提取	快速傅里叶变换低信噪比误差大单目标检测为主	AI增强信号去噪能力
计算效率^[16,22]	边缘智能降低30%～60%时延实时信道建模	云端集中计算时延高多径分离需迭代处理	云边端协同架构优化
时空频复杂度	LSTM波束预测控制时延动态频谱共享	凸优化算法耗时长固定子载波分配	AI动态资源调度技术
能耗	AI辅助降低功耗	全子载波高功耗	智能功率优化策略

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表 3 AI赋能通感算一体化系统关键技术简要汇总

参考文献	关键技术	AI作用	性能指标	训练模型	应用场景
[25]	波形设计	优化波形生成、选择、调整、匹配等，以适应通信感知双重需求，并降低复杂度	保密率	DRL等	自动驾驶
[21,26]	波束赋形	提高了频谱效率，减轻了多径衰落，确保了动态城市环境中的无缝连接和可靠性	和速率	DRL, DL等	自动驾驶
[28–30]	信道估计	提升信道估计的精度、降低计算复杂性，实现动态适配	估计精度	GAN, CNN等	自动驾驶
[32]	干扰管理	在资源有限场景中，实时应对并缓解通信与感知任务中的干扰问题	均方误差	DNN, ML等	无人机监测
[33,34]	动态频谱分配	提供智能化的优化算法和学习模型，实现高效的动态分配，提升系统性能	准确率、频谱效率	DRL, RNN等	工业物联网

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