Abstract

Abstract
Conventional power distribution networks (PDNs), in which individual voltage regulators power the entire integrated circuit (IC), are ineffective for high-power, large-area ICs. In highperformance systems-on-chip (SoCs) and microprocessors (in particular those designed for AI applications), shrinking technology nodes are leading to higher current densities, which impose thermal constraints and limit the portion of the chip that can be simultaneously powered (“dark silicon”). PDNs with point-of-load regulation offer a promising alternative. The distributed nature of their design inherently relaxes thermal constraints while minimizing high-current routing overhead (IR drops), thereby improving the PDN efficiency. In this work, the concept of on-chip distributed voltage regulation is introduced. Previously reported distributed voltage regulator designs are reviewed, emphasizing their major achievements and limitations. Then, the challenges that hinder a more ubiquitous adoption of such designs, namely stability (analysis) and unbalanced load sharing, are discussed. Existing solutions addressing these challenges are also presented. Finally, a comparative analysis of the performance of these regulators is presented, and insights into the future direction of distributed voltage regulation are offered. © (2025), (Universidade do Porto - Faculdade de Engenharia). All rights reserved.

Abstract
For the upcoming 6G wireless networks, reconfigurable intelligent surfaces are an essential technology, enabling dynamic beamforming and signal manipulation in both reflective and transmissive modes. It is expected to utilize frequency bands in the millimeter-wave and THz, which presents unique opportunities but also significant challenges. The selection of switching technologies that can support high-frequency operation with minimal loss and high efficiency is particularly complex. In this work, we demonstrate the potential of advanced components such as Schottky diodes, memristor switches, liquid metal-based switches, phase change materials, and RF-SOI technology in RIS designs as an alternative to overcome limitations inherent in traditional technologies in D-band (110-170 GHz).

Abstract
The technology of Reconfigurable Intelligent Surfaces (RISs) is lately being considered as a boosting component for various indoor wireless applications, enabling wave propagation control and coverage extension. However, the incorporation of extremely large RISs, as recently being considered for ultra-high capacity industrial environments at subTHz frequencies, imposes certain challenges for indoor channel characterization. In particular, such RISs contribute additional multipath components and their large sizes with respect to the signal wavelength lead to near-field propagation. To this end, ray tracing approaches become quite cumbersome and need to be rerun for different RIS unit cell designs. In this paper, we present a novel approach for the incorporation of RISs in indoor multipath environments towards their efficient channel characterization. An 100x100 RIS design with 2-bit resolution unit cells realizing a fixed anomalous reflection at 300 GHz is presented, whose radar cross section patterns are obtained via full-wave simulations. It is showcased that the RIS behavior can be conveniently approximated by a three-ray model, which can be efficiently incorporated within available ray tracing tools, and that the far-field approximation is valid for even very small distances from the RIS.

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.