计算赋能通感一体化关键技术研究
Research on Key Technologies in Integrated Sensing and Communication Empowered by Computing
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摘要: 快速发展的第六代移动通信技术(Sixth Generation, 6G),有望实现对传统数据中心服务的革命性超越,以支撑“万物智能互联”的新型网络生态,开启泛在智能服务的未来社会。在此背景下,通信、感知与计算三大功能的深度融合成为构建6G智能网络的核心路径,然而,这些功能相互依存,在频谱、能量和空间资源方面形成了资源竞争。通感算一体化(Integrated Sensing, Communication, Computing, ISCC)旨在通过资源共享与协同优化,实现环境感知、信息传输与数据处理的有机统一,全面提升网络效率与智能化水平。首先,本文从信号设计角度出发,系统分析了通感算一体化在物理层的关键技术,包括基于通信信号与感知信号的双向设计思路、空中计算辅助的通信计算融合机制,以及感知、通信与计算三功能信号的协同波束与波形优化,实现多功能在同一物理资源上的高效集成。其次,针对系统级网络资源管理,本文从频谱、时隙、空间和计算卸载四个维度总结了资源动态管控方法,并区分了任务共存式与面向任务式两种ISCC资源分配范式,揭示了跨层协同优化的内在机制。最后,提出了新型传输范式、人工智能赋能及数字孪生驱动的通感算一体化发展方向,为推动ISCC从理论走向实际部署提供系统性参考,助力6G释放智能连接的全部潜力。Abstract: The rapidly evolving sixth-generation (6G) telecommunications framework is poised to revolutionize traditional data-centric services. The new protocol is expected to support a novel network ecosystem of ubiquitous intelligent connectivity and usher in a future society characterized by pervasive intelligent services. In this context, the deep integration of three major functions has emerged as the core pathway to construct intelligent 6G networks, including communication, sensing, and computing. However, these functions are interdependent, which tends to result in resource competition across spectrum, energy, and space. Integrated sensing, communication, and computation (ISCC) methods have been developed to achieve an organic unification of environmental sensing, information transmission, and data processing through resource sharing and collaborative optimization to comprehensively enhance the efficiency and intelligence of network systems. In this study, we analyzed the key technologies of ISCC at the physical layer in terms of signal design, including bidirectional approaches based on communication and sensing signals. We also developed a communication-computation fusion mechanism assisted by over-the-air computation as well as collaborative beamforming and waveform optimization for three-function sensing, communication, and computation signals to integrate multiple functions on the same physical resources efficiently. Secondly, with regard to system-level network resource management, we summarize dynamic resource control methods in terms of four dimensions, including spectrum, time slots, space, and computation offloading. We also distinguish between two ISCC resource allocation paradigms referred to as task coexistence and task-oriented allocation, and our results revealed the intrinsic mechanisms of cross-layer collaborative optimization. Finally, we suggest some possible directions for the development of ISCC systems driven by novel transmission paradigms, artificial intelligence, and digital twins. Thus, this work provides a systematic reference to advance ISCC from theory to practical deployment and supports the development of 6G systems to unleash the full potential of intelligent connectivity.
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