PHY2 – ISAC, Radar and Localization
Wednesday, 3 June 2026, 17:00-18:30, room Sala de Conferencias 2 (2.1) (1st floor)
Session Chair: Teresa María Martín-Guerrero (Univ. Málaga, ES)
1-Bit Nearfield Angle-Range Estimation with Massive Uniform Linear Array
Shrayan Das and Marko E Leinonen (University of Oulu, Finland)
Next-generation communication systems operating in the mm-Wave and sub-THz bands face high path loss, which can be mitigated by ultra-massive antenna arrays. However, highresolution quantization in these systems is often impractical, leading to a growing interest in low-resolution, particularly 1-bit ADCs. This work addresses joint angle and range estimation using 1-bit massive uniform linear arrays (ULA) in narrowband near-field systems. We propose a 2D 1-bit MUSIC for joint angle-range estimation, thus demonstrating that existing MUSIC-based methods can be adapted for effective near-field localization even under extreme quantization. Subsequently, two low-complexity variants of the 2D 1-bit MUSIC, namely, the reconstructed and approximated 1-bit joint angle-range estimation MUSIC (JARE-MUSIC) are proposed for sequential angle-range estimation. Simulation results indicate that both 1-bit JARE-MUSIC variants demonstrate localization accuracy close to that of full-resolution 2D MUSIC, especially when large arrays and long training sequences are employed.
Analysis of over-the-Air Frequency and Time Synchronization Requirements for Cooperative ISAC Radio Units with an RF Crystal Oscillator
Robin Lohuis, Erwin Hardeveld and Eric Klumperink (University of Twente, The Netherlands)
Cell-free densified networks of radio units (RUs) leveraging cooperative distributed multiple-input multiple-output (D-MIMO) can offer high data capacity per area, low latency, and uniform coverage. As fiber-access cannot be taken for granted in dense employments, some wireless RUs are required to use over-the-air synchronization (OAS). Distributed networks of cooperative RUs can also support multi-static integrated sensing and communication (ISAC) provided RUs are accurately synchronized. Frequency synthesizer circuits are typically optimized for short-term stability (jitter and phase noise), but long-term stability is also critical to limit timing drift between synchronization updates. At higher radio frequency (RF) carrier frequencies, equal jitter results in tighter phase-noise requirements for frequency references. This motivates the use of RF crystal oscillators (RFXOs) to achieve the required sub-100 fs jitter performance for 6G complex modulation schemes at centimeter-wave frequencies, but such crystals have a worse long-term stability, which we aim to evaluate. Long-term stability can be evaluated using two-sample variances such as Allan deviation (ADEV) and time deviation (TDEV). Combining these metrics enables the translation of system-level targets, such as 1 ppb frequency stability and 4 ps time error into synchronization-interval requirements. Using simulation results of a representative frequency synthesizer, we compute the resulting TDEV and design a synchronization loop that improves long-term stability. Our results show that for RFXO-based RUs, our ADEV and TDEV requirements can still be met at millisecond-scale synchronization intervals assuming an Additive White Gaussian Noise (AWGN) channel.
Opportunistic 3D Radar Positioning with MIMO-OFDM for ISAC
Jean-Baptiste Doré (CEA-Leti, France); David Demmer (CEA-Leti, Université Grenoble Alpes, France); Benoit Denis (CEA-Leti & Université Grenoble Alpes, France)
This paper presents an opportunistic 3D radar positioning method for monostatic Multiple-Input Multiple-Output (MIMO) Orthogonal Frequency Division Multiplexing (OFDM)-based Integrated Sensing and Communication (ISAC) systems. By leveraging only communication signals, the proposed model-based algorithm jointly estimates the 3D positions and Radar Cross Sections (RCSs) of multiple targets. The approach is fully opportunistic, as it relies solely on existing communication signals transmitted and received by the same transceiver, without requiring dedicated radar resources in space, time, or frequency. To manage computational complexity, a three-step processing framework is introduced: a coarse grid search, followed by a Constant False Alarm Rate (CFAR)-based decision stage for clutter removal, and a 3D refined search using gradient descent optimization. Detection performance is numerically evaluated and demonstrate accurate estimation of target reflection coefficients, while the accuracy of 3D target localization depends on the antenna geometry and orientation.
Phaseless near-Field Localization with Non-Isotropic Receive Antennas
Nitin Jonathan Myers (Delft University of Technology, The Netherlands); Davide Scazzoli and Umberto Spagnolini (Politecnico di Milano, Italy)
Near-field localization is a key enabler for short range communications and sensing applications. Most localization methods rely on the phase of received signals, necessitating carrier synchronization or high-rate sampling with coherent receivers. Non-coherent receivers have a substantially lower hardware complexity, albeit at the cost of losing phase information. In this paper, we develop a maximum likelihood-based algorithm for near-field localization from phaseless measurements. Our method leverages spatial variations in the signal strength across the non-coherent receiver under antenna pattern non idealities to localize the source. Using simulations as well as experiments in the 5G FR3 band, we demonstrate that our algorithm significantly outperforms comparable benchmarks at short distances.
RIS-Aided Sensing: Experimental Validation of Radar 3D Imaging in the mmWave Band
Sergio Micó-Rosa (Universitat Politecnica de Valencia, Spain); Alvaro Villaescusa-Tebar (Universitat Politècnica de València, Spain); Saúl Fenollosa, Carlos Villena-Jiménez and Monika Drozdowska (Universitat Politecnica de Valencia, Spain); Narcis Cardona (The Polytechnic University of Valencia, Spain)
The transition toward 6G networks demands energy-efficient hardware capable of active interaction with the environment. Reconfigurable Intelligent Surfaces (RIS) have emerged as a key technology for Integrated Sensing and Communications (ISAC), enabling geometric environment recognition with minimal power consumption. However, achieving targeted 3D spatial mapping in a fully autonomous, closed-loop system remains a significant challenge. In this work, we validate experimentally an autonomous mmWave 3D imaging framework that integrates an Frequency-Modulated Continuous Wave (FMCW) radar with a 1-bit RIS and a Vector Network Analyzer (VNA) to perform targeted 3D reconstruction. The FMCW radar acts as a coarse localizer, providing real-time spatial priors to define dynamic Regions of Interest (ROI). These coordinates are translated into optimized RIS phase profiles to perform Stepped-Frequency Continuous-Wave (SFCW) measurements. We experimentally validate the system through three diverse scenarios, including metallic mannequins, calibration spheres, and a complex multi-target environment containing human subjects and an Automated Guided Vehicle (AGV). The results demonstrate accurate 3D voxel-based reconstruction of targets even at reduced angular resolutions, advancing the feasibility of RIS-based sensing for industrial and security applications.
