radar

To meet 6G Integrated Sensing and Communication (ISaC) requirements, this paper addresses the architectural adaptation challenges arising from reusing communication antennas for radar sensing signals. Method: We propose the first systematic, hierarchical functional decomposition and standardized interface specification for ISaC. Specifically, we introduce a dedicated sensing signal processing entity, integrate findings from the KOMSENS-6G architecture working group, and employ functional modularization, interface standardization, and joint communication-sensing signal processing techniques. Contribution/Results: The core outcome is an extensible ISaC reference architecture white paper, which—uniquely at the 6G network level—precisely defines functional boundaries and coordination mechanisms for communication-sensing integration. This enables efficient antenna resource sharing and cross-domain joint processing. The architecture has been adopted as the alignment baseline and technical discussion starting point for multiple international 6G innovation initiatives.

To address the functional isolation and lack of coordination between communication and synthetic aperture radar (SAR) remote sensing in low Earth orbit (LEO) satellite systems, this paper proposes an integrated sensing and communication (ISAC) architecture. We design a unified waveform based on orthogonal time-delay–Doppler multiplexing (ODDM) and develop a joint transmission protocol compatible with 5G New Radio (NR) standards. A unified signal processing framework is established to jointly support channel estimation, interference suppression, and real-time SAR imaging. Leveraging software-defined radio (SDR), the system achieves dual-band compatibility across sub-6 GHz and millimeter-wave (mmWave) frequencies. Simulation results demonstrate superior performance in the sub-6 GHz band, while an mmWave SDR prototype successfully validates real-time SAR imaging and data transmission to user equipment. To the best of our knowledge, this work presents the first LEO satellite ISAC system featuring shared RF front-end hardware and deep integration at both waveform and protocol levels.

研究ISAC系统中三维大目标的感知与通信融合，通过模型分析与数学优化，设计信号加强方案，提出图神经网络波束形成方法，实现感知通信最优平衡，性能优于传统方法。

This work addresses the challenge of radar-free UAV detection in urban microcell (UMi) and macrocell (UMa) environments using standardized 5G NR Positioning Reference Signals (PRS). We propose an end-to-end native radar processing chain integrating time-frequency joint clutter suppression, MUSIC-based angle estimation, matched-filter ranging, and geometric triangulation. To our knowledge, this is the first systematic validation—under standardized 3GPP channel models—of 5G NR native radar capability for aerial target detection. A fully open-source simulation platform is released to enable reproducible ISAC research. Experimental results show positioning errors ≤4 m (with ≤16% missed-detection rate) in UMi scenarios and ≤8 m in UMa scenarios; errors increase with target range and altitude. The core contribution lies in the first demonstration of standardized PRS waveforms as high-accuracy aerial sensing enablers, advancing practical integration of communication and sensing (ISAC).

To address the lack of range dimension and consequent difficulty in achieving high-accuracy joint sensing in far-field integrated sensing and communication (ISAC) systems, this paper proposes a near-field ISAC framework. We first derive the Cramér–Rao lower bound (CRLB) for joint range-angle estimation under near-field ISAC—marking the first theoretical characterization of estimation accuracy in this regime. Departing from the far-field plane-wave assumption, we establish an exact near-field channel model. Furthermore, we formulate a joint waveform and two-stage hybrid beamforming optimization problem for both fully digital and hybrid antenna arrays, minimizing the CRLB subject to minimum user communication rate constraints. The problem is efficiently solved via semidefinite relaxation (SDR). Numerical results demonstrate that the proposed design significantly enhances range resolution and angle estimation accuracy, while improving the communication-sensing trade-off—thereby providing both a verifiable theoretical foundation and a practical design paradigm for near-field ISAC.

This work investigates the joint optimization of array geometry and waveform design in active sensing systems to approach the Cramér-Rao bound (CRB) performance limit for parameter estimation, balancing mean squared error and identifiability. By analyzing the CRB under both orthogonal and coherent waveforms for linear and planar arrays, it reveals that single-target estimation performance is governed by the sum of the spatial variances of the transmit and receive arrays. Building on this insight, the study proposes an asymmetric allocation strategy of transmit and receive sensors, departing from conventional symmetric designs. It further establishes a connection between Diophantine equations and CRB-equivalent array constructions, leveraging weighted virtual array multiplicity and beam steering optimization to derive a general optimality criterion. This yields constructible high-performance array configurations, offering a new paradigm for MIMO active sensing systems.

This work addresses the challenge of velocity estimation in robot-assisted integrated sensing and communication (ISAC) systems, where mobile robots can only opportunistically reuse irregular 5G/6G reference signals, limiting the performance of conventional methods. The study is the first to reveal the structural characteristics of the velocity spectrum under such irregular reference signaling, decomposing it into a periodic peak component and an amplitude-weighted component. Building on this insight, the authors propose a multi-periodogram-based velocity estimation algorithm that requires no dedicated sensing signals or modifications to the 3GPP protocol, ensuring full compatibility with existing standards. Experimental results demonstrate that, at a 10% miss-detection rate, the proposed method achieves a 3 dB SNR gain over traditional periodogram approaches and reduces the false alarm rate by 51%.

This work addresses high-precision sensing of geometric parameters—specifically shape, distance, and incident direction—of obstacles in the environment by exploiting wireless signal diffraction. The study proposes a frequency-agnostic, parameterized diffraction channel modeling framework that unifies multiple wave propagation approximations, including far-field, paraxial Fresnel, and exact near-field regimes. A scaling law based on the Fresnel number is established to enable consistent mapping of diffraction patterns across varying frequencies, object sizes, and distances. Parameter inversion is performed via maximum likelihood estimation, and the Cramér–Rao Bound (CRB) is employed to rigorously quantify the fundamental performance limits of diffraction-based sensing. Experimental results demonstrate that, under moderate to high signal-to-noise ratios, the estimation accuracy approaches the CRB, thereby validating both the effectiveness and theoretical optimality of the proposed method for high-fidelity obstacle characterization.

This work addresses the limitations of existing RF measurement platforms, which rely on laboratory equipment or fixed infrastructure and thus lack the flexibility required for prolonged field-based spectrum monitoring. The authors propose a portable, battery-powered RF acquisition system integrating a HackRF One software-defined radio, a Raspberry Pi 5, a GNSS receiver, and a high-speed SSD to enable continuous IQ data recording with precise spatiotemporal metadata. Data are stored in the SigMF format at sustained write throughput of 75–85 MB/s without sample loss, while GNSS synchronization achieves timing errors under one second and meter-level positioning accuracy. Field experiments successfully captured characteristic propagation effects at 2.45 GHz—including vegetation attenuation, urban multipath, and indoor interference—demonstrating significantly enhanced deployment flexibility, environmental adaptability, and data fidelity for real-world RF sensing.

This study addresses the critical need for efficient radar signal detection in the 3.5 GHz Citizens Broadband Radio Service (CBRS) shared spectrum to prevent commercial communications from interfering with military radar operations. The work provides a systematic review of existing detection techniques, encompassing regulatory requirements, radar signal characteristics, and both traditional and machine learning–based approaches, offering the first comprehensive performance comparison between these paradigms under complex electromagnetic conditions. Building upon the Environmental Sensing Capability (ESC) sensor network architecture, the authors propose a hybrid strategy that integrates energy detection, template matching, and deep learning to achieve high detection accuracy, low latency, and robustness. Experimental results demonstrate that learning-based methods significantly outperform conventional techniques in challenging scenarios, attaining a 99% probability of detection with response times under 60 seconds, while also outlining viable pathways to address remaining challenges such as false alarm suppression and real-time performance guarantees.