面向低空无人机且零感知资源分配的多用户MIMO-OFDM ISAC全维波束赋形
Full-Dimensional Beamforming for Multi-User MIMO-OFDM ISAC for Low-Altitude UAV with Zero Sensing Resource Allocation
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中文总结 AI 辅助
本文针对低空无人机ISAC系统中专用感知资源导致通信频谱效率下降的问题，提出一种零感知时频资源开销的MIMO-OFDM框架，通过联合发射波束赋形与低复杂度目标搜索及两阶段超分辨率感知算法，在提升通信和速率的同时改善感知分辨率、无模糊距离和精度。
中文摘要 AI 辅助
低空无人机（UAV）有望在低空经济中发挥重要作用，其应用范围广泛，包括精准农业、空中配送和监视。集成感知与通信（ISAC）是一项关键技术，通过高效地同时提供通信和感知服务，能够支持无人机的大规模部署和日常使用。对于无人机ISAC系统，由于无人机通常同时充当通信用户设备（UE）和感知目标，传统ISAC系统通常为感知分配专用的时频（TF）资源，这会严重降低通信频谱效率，因而效率低下。为了解决这一问题，本文提出了一种新颖的基于多输入多输出（MIMO）正交频分复用（OFDM）的无人机ISAC框架，该框架无需专用的感知TF资源，实现了零TF感知开销。通过设计发射波束赋形以满足通信和感知任务的要求，所提出的方法使通信TF资源能够被完全复用于感知，从而在分辨率、无模糊距离和精度方面同时提升通信和速率与感知性能。此外，我们引入了一种低复杂度的目标搜索波束赋形算法和一种两阶段超分辨率感知算法，以确保高效实现。仿真结果表明，所提出的MIMO-OFDM-ISAC框架不仅提高了通信和速率，而且在感知性能上优于传统ISAC系统，使其成为未来支持低空无人机的ISAC系统的一种有前景的解决方案。
英文摘要
Low-altitude unmanned aerial vehicles (UAVs) are expected to play an important role for low-altitude economy with a wide range of applications like precise agriculture, aerial delivery and surveillance. Integrated sensing and communication (ISAC) is a key technology to enable the large-scale deployment and routine usage of UAVs by providing both communication and sensing services efficiently. For UAV ISAC systems, as UAV often acts as both a communication user equipment (UE) and a sensing target, traditional ISAC systems that usually allocate dedicated TF resources for sensing are inefficient due to the severe degradation of communication spectral efficiency. To address this issue, in this paper, we propose a novel multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM)-based ISAC framework for UAVs that eliminates the need for dedicated sensing TF resources, achieving zero TF sensing overhead. By designing the transmit beamforming to meet the requirements for both communication and sensing tasks, our proposed approach enables the communication TF resources to be fully reused for sensing, thereby enhancing both the communication sum rate and the sensing performance in terms of resolution, unambiguous range, and accuracy. Additionally, we introduce a low-complexity target searching beamforming algorithm and a two-stage super-resolution sensing algorithm, which ensure efficient implementation. Simulation results demonstrate that the proposed MIMO-OFDM-ISAC framework not only improves the communication sum rate but also outperforms traditional ISAC systems in sensing performance, making it a promising solution for future ISAC systems to support low-altitude UAVs.
