Full-Dimensional Beamforming for Multi-User MIMO-OFDM ISAC for Low-Altitude UAV with Zero Sensing Resource Allocation

TL;DR

This paper introduces a MIMO-OFDM ISAC framework for low-altitude UAVs that eliminates dedicated sensing resources, enhancing both communication and sensing performance through innovative beamforming and algorithms.

Contribution

It proposes a novel zero-resource sensing approach for UAV ISAC systems, enabling full reuse of communication resources for sensing without dedicated spectrum allocation.

Findings

Improves communication sum rate compared to traditional ISAC systems.

Enhances sensing resolution, range, and accuracy.

Demonstrates effectiveness through simulation results.

Abstract

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.