RIS-Aided Sensing: Experimental Validation of Radar 3D Imaging in the mmWave Band

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.

Evolution Strategies for Deep RL pretraining

Although Deep Reinforcement Learning has proven highly effective for complex decision-making problems, it demands significant computational resources and careful parameter adjustment in order to develop successful strategies. Evolution strategies offer a more straightforward, derivative-free approach that is less computationally costly and simpler to deploy. However, ES generally do not match the performance levels achieved by DRL, which calls into question their suitability for more demanding scenarios. This study examines the performance of ES and DRL across tasks of varying difficulty, including Flappy Bird, Breakout and Mujoco environments, as well as whether ES could be used for initial training to enhance DRL algorithms. The results indicate that ES do not consistently train faster than DRL. When used as a preliminary training step, they only provide benefits in less complex environments (Flappy Bird) and show minimal or no improvement in training efficiency or stability across different parameter settings when applied to more sophisticated tasks (Breakout and MuJoCo Walker).

Deterministic Modeling of Dynamic ISAC Channels in RF Digital Twin Environments

This paper introduces a methodology to calibrate Radio-Frequency Digital Twins (RF-DTs) for Integrated Sensing and Communication (ISAC) in dynamic wireless environments. The approach leverages high-resolution ray tracing in combination with wideband channel sounding to ensure consistency between simulated and measured propagation. The methodology is validated in urban scenarios featuring both mono-static and bi-static configurations, as well as moving user platforms and vehicles. Results show that the calibrated RF-DT reproduces key propagation effects, including multipath evolution, dynamic scatterers, and Doppler-induced signatures, with close agreement to measurements. These findings confirm that accurate geometry, material modeling, antenna patterns, and diffuse scattering are essential for realistic high-frequency ISAC simulation. By bridging the gap between simulation and measurement, the proposed calibration framework provides a scalable tool for developing and evaluating ISAC algorithms in complex, time-varying environments envisioned for 6G.

Correlation and Temporal Consistency Analysis of Mono-static and Bi-static ISAC Channels

Integrated Sensing and Communication (ISAC) is critical for efficient spectrum and hardware utilization in future wireless networks like 6G. However, existing channel models lack comprehensive characterization of ISAC-specific dynamics, particularly the relationship between mono-static (co-located Tx/Rx) and bi-static (separated Tx/Rx) sensing configurations. Empirical measurements in dynamic urban microcell (UMi) environments using a 79-GHz FMCW channel sounder help bridge this gap. Two key findings are demonstrated: (1) mono-static and bi-static channels exhibit consistently low instantaneous correlation due to divergent propagation geometries; (2) despite low instantaneous correlation, both channels share unified temporal consistency, evolving predictably under environmental kinematics. These insights, validated across seven real-world scenarios with moving targets/transceivers, inform robust ISAC system design and future standardization.

Cell Sensing: Traffic detection

This work presents a passive sensing system for traffic monitoring using ambient Long Term Evolution (LTE) signals as a non-intrusive and scalable alternative to traditional surveillance methods. The approach employs a dual-receiver architecture analyzing Channel State Information (CSI) to isolate differential Doppler shifts induced by moving targets, effectively mitigating hardware-induced phase impairments. Implemented with a Software Defined Radio (SDR) platform and srsRAN software, the system demonstrated over 90% detection accuracy for speeds above 6000 mm/min in controlled indoor tests, and provided reliable speed estimations for pedestrians and vehicles in outdoor evaluations. Despite challenges at low speeds, directional ambiguity, and multipath fading in urban settings, the results validate LTE-based passive sensing as a feasible traffic monitoring method, identifying critical areas for future research such as angle-of-arrival (AoA) integration, machine learning, and real-time embedded system development.

Novel Approach to Dual-Channel Estimation in Integrated Sensing and Communications for 6G

Integrated Sensing and Communication (ISAC) design is crucial for 6G and harmonizes environmental data sensing with communication, emphasizing the need to understand and model these elements. This paper delves into dual-channel models for ISAC, employing channel extraction techniques to validate and enhance accuracy. Focusing on millimeter wave (mmWave) radars, it explores the extraction of the bistatic sensing channel from monostatic measurements and subsequent communication channel estimation. The proposed methods involve interference extraction, module and phase correlation analyses, chirp clustering, and auto-clutter reduction. A comprehensive set-up in an anechoic chamber with controlled scenarios evaluates the proposed techniques, demonstrating successful channel extraction and validation through Root Mean Square Delay Spread (RMS DS), Power Delay Profile (PDP), and Angle of Arrival (AoA) analysis. Comparison with Ray-Tracing (RT) simulations confirms the effectiveness of the proposed approach, presenting an innovative stride towards fully integrated sensing and communication in future networks.

Frequency-responsive RCS characteristics and scaling implications for ISAC development

This paper presents an investigation on the Radar Cross-Section (RCS) of various targets, with the objective of analysing how RCS properties vary with frequency. Targets such as an Automated Guided Vehicle (AGV), a pedestrian, and a full-scale car were measured in the frequency bands referred to in industry standards as FR2 and FR3. Measurements were taken in diverse environments, indoors and outdoors, to ensure comprehensive scenario coverage. The methodology employed in RCS extraction performs background subtraction, followed by time-domain gating to isolate the influence of the target. This analysis compares the RCS values and how the points of greatest contribution are distributed across different bands based on the range response of the RCS. Analysis of the results demonstrated how RCS values change with frequency and target shape, providing insights into the electromagnetic behaviour of these targets. Key findings highlight how much scaling RCS values based on frequency and geometry is complex and varies among different types of materials and shapes. These insights are instrumental for advancing sensing systems and enhancing 3GPP channel models, particularly for Integrated Sensing and Communications (ISAC) techniques proposed for 6G standards.