Abstract

Abstract
This paper introduces a novel service-oriented framework, Radio Propagation as a Service (RPaaS), that bridges the gap between raw sensor data and high-fidelity wireless channel simulations. RPaaS transforms noisy, sensor-derived point clouds into accurate 3D models through robust registration, segmentation, and edge detection. These models then feed into a GPU-accelerated ray tracing engine that computes multipath propagation effects, while a separate module derives key electromagnetic and channel parameters. All components are orchestrated via a REST API in a Dockerized environment, enabling dynamic reconfiguration based on sensor data conditions. Experimental validation against commercial ray tracing tools and channel measurements demonstrates that our approach provides accurate simulations even in the presence of sensor noise. © 2025 European Signal Processing Conference, EUSIPCO. All rights reserved.

Abstract
Telecommunications and computer vision have evolved independently. With the emergence of high-frequency wireless links operating mostly in line-of-sight, visual data can help predict the channel dynamics by detecting obstacles and help overcoming them through beamforming or handover techniques. This paper proposes a novel architecture for delivering real-time radio and video sensing information to O-RAN xApps through a multi-agent approach, and introduces a new video function capable of generating blockage information for xApps, enabling Integrated Sensing and Communications. Experimental results show that the delay of sensing information remains under 1 ms and that an xApp can successfully use radio and video sensing information to control the 5G/6G RAN in real-time.

Abstract
Telecommunications and computer vision solutions have evolved significantly in recent years, allowing a huge advance in the functionalities and applications offered. However, these two fields have been making their way as separate areas, not exploring the potential benefits of merging the innovations brought from each of them. In challenging environments, for example, combining radio sensing and computer vision can strongly contribute to solving problems such as those introduced by obstructions or limited lighting. Machine learning algorithms, able to fuse heterogeneous and multi-modal data, are also a key element for understanding and inferring additional knowledge from raw and low-level data, able to create a new abstracting level that can significantly enhance many applications. This paper introduces the CONVERGE vision-radio concept, a new paradigm that explores the benefits of integrating two fields of knowledge towards the vision of View-to-Communicate, Communicate-to-View. The main concepts behind this vision, including supporting use cases and the proposed architecture, are presented. CONVERGE introduces a set of tools integrating wireless communications and computer vision to create a novel experimental infrastructure that will provide open datasets to the scientific community of both experimental and simulated data, enabling new research addressing various 6 G verticals, including telecommunications, automotive, manufacturing, media, and health.

Abstract
Research on Programmable Electromagnetic Surfaces has gained considerable attention as an enabling technology for 6G communications, particularly at millimeter-wave and sub-THz bands. However, RISs face challenges related to the need for high-performance reconfigurable techniques that offer compact size and reduced power consumption at high frequencies. Moreover, the experimental characterization of unit cell performance using a waveguide remains a challenging issue. This paper discusses the design and performance analysis of a 1-bit reconfigurable unit cell at the Dband using non-volatile reconfigurable technology. The efficiency analysis of the unit cell was performed using periodic boundary conditions and waveguide configurations to mitigate simulation risks and validate the proposed design at the unit cell level. All simulation configurations confirmed an operational bandwidth of 25.65 GHz across the 147.8-173.45 GHz range, with a reflection loss of less than 1 dB and a phase difference within 180 degrees +/- 20 degrees.

Abstract
Reconfigurable Intelligent Surfaces (RISs) are expected to play a pivotal role in future indoor ultra high data rate wireless communications as well as highly accurate three-dimensional localization and sensing, mainly due to their capability to provide flexible, cost- and power-efficient coverage extension, even under blockage conditions. However, when considering beyond millimeter wave frequencies where there exists GHz-level available bandwidth, realistic models of indoor RIS-parameterized channels verified by field-trial measurements are unavailable. In this article, we first present and characterize three RIS prototypes with unit cells of half-wavelength intercell spacing, which were optimized to offer a specific nonspecular reflection with 1-, 2-, and 3-bit phase quantization at 304 GHz. The designed static RISs were considered in an indoor channel measurement campaign carried out with a 304 GHz channel sounder. Channel measurements for two setups, one focusing on the transmitter-RIS-receiver path gain and the other on the angular spread of multipath components, are presented and compared with both state-of-the-art theoretical models as well as full-wave simulation results. The article is concluded with a list of challenges and research directions for RIS design and modeling of RIS-parameterized channels at THz frequencies.