A Two-Stage ISAC Framework for Low-Altitude Economy Based on 5G NR Signals

The evolution of next-generation wireless networks has spurred the vigorous development of the low-altitude economy (LAE). To support this emerging field while remaining compatible with existing network architectures, integrated sensing and communication (ISAC) based on 5G New Radio (NR) signals is regarded as a promising solution. However, merely leveraging standard 5G NR signals, such as the Synchronization Signal Block (SSB), presents fundamental limitations in sensing resolution. To address the issue, this paper proposes a two-stage coarse-to-fine sensing framework that utilizes standard 5G NR initial access signals augmented by a custom-designed sparse pilot structure (SPS) for high-precision unmanned aerial vehicles (UAV) sensing. In Stage I, we first fuse information from the SSB, Type\#0-PDCCH, and system information block 1 (SIB1) to ensure the initial target detection. In Stage II, a refined estimation algorithm is introduced to overcome the resolution limitations of these signals. Inspired by the sparse array theory, this stage employs a novel SPS, which is inserted into resource blocks (RBs) within the CORSET\#0 bandwidth. To accurately extract the off-grid range and velocity parameters from these sparse pilots, we develop a corresponding high-resolution algorithm based on the weighted unwrapped phase (WUP) technique and the RELAX-based iterative method. Finally, the density-based spatial clustering of applications with noise (DBSCAN) algorithm is adopted to prune the redundant detections arising from beam overlap. Comprehensive simulation results demonstrate the superior estimation accuracy and computational efficiency of the proposed framework in comparison to other techniques.

4D Imaging in ISAC Systems: A Framework Based on 5G NR Downlink Signals

Integrated sensing and communication (ISAC) has emerged as a key enabler for sixth-generation (6G) wireless networks, supporting spectrum sharing and hardware integration. Beyond communication enhancement, ISAC also enables high-accuracy environment reconstruction and imaging, which are crucial for applications such as autonomous driving and digital twins. This paper proposes a 4D imaging framework fully compliant with the 5G New Radio (NR) protocol, ensuring compatibility with cellular systems. Specifically, we develop an end-to-end processing chain that covers waveform generation, echo processing, and multi-BS point cloud fusion. Furthermore, we introduce Zoom-OMP, a coarse-to-fine sparse recovery algorithm for high-resolution angle estimation that achieves high accuracy with reduced computational cost. The simulation results demonstrate that the proposed framework achieves robust 4D imaging performance with superior spatial accuracy and reconstruction quality compared to conventional benchmarks, paving the way for practical ISAC-enabled environment reconstruction in 6G networks.

Movable Antenna-enabled RIS-aided Integrated Sensing and Communication

In this paper, we investigate a movable antenna (MA)-aided integrated sensing and communication (ISAC) system, where a reconfigurable intelligent surface (RIS) is employed to enhance wireless communication and sensing performance in dead zones. Specifically, this paper aims to maximize the minimum beampattern gain at the RIS by jointly optimizing beamforming matrix at the base station (BS), the reflecting coefficients at the RIS and the positions of the MAs, subject to signal-to-interference-plus-noise ratio (SINR) constraint for the users and maximum transmit power at the BS. To tackle this non-convex optimization problem, we propose an alternating optimization (AO) algorithm and employ semidefinite relaxation (SDR), sequential rank-one constraint relaxation (SRCR) and successive convex approximation (SCA) techniques. Numerical results indicate that the MA and RIS-aided ISAC system outperforms conventional fixed position antenna (FPA) and RIS-aided systems. In addition, the application of MAs can reduce the similarity of user channels and enhance channel gain in the ISAC system.