Enhanced Programmability of Reconfigurable Intelligent Surfaces for Smart Cities using Application-Specific Integrated Circuits

Reconfigurable Intelligent Surfaces (RISs) are emerging as a key technology for enhancing wireless communications in smart city environments, offering robust, low-latency connectivity. This paper presents the design of an adaptive control system based on high-voltage, mixed-signal integrated circuits for controlling tunable RISs, tailored for wireless cellular communications in urban areas. The proposed system enables enhanced programmability by dynamically steering beams and fine-tuning signal reflections through the precise control of varactor diodes embedded within the RIS unit cells. This flexible system architecture supports real-time software reconfiguration, allowing RIS units to adapt to varying network conditions and scale seamlessly across smart city infrastructures. Simulated results on a 0.35 μm CMOS high voltage process technology demonstrate the circuit's ability to achieve low power consumption and high reconfiguration speed across a wide temperature range. The control electronics are separated from the RIS panels, this way enabling tiling of multiple surfaces for scalable, cost-effective deployment. This approach offers a promising pathway to meeting the growing demand for seamless, high-speed, and energy-efficient connectivity in next-generation smart city applications, including vehicular networks, Internet of Things, smart traffic management and smart infrastructure.

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Published
2025-05-25
DOI
https://doi.org/10.1109/iscas56072.2025.11043875
Primary Topic
Modular Robots and Swarm Intelligence
Type
article
Field-Weighted Citation Impact
0.00

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article

Enhanced Programmability of Reconfigurable Intelligent Surfaces for Smart Cities using Application-Specific Integrated Circuits

Yorgos Stratakos, Marco A. Antoniades, Loukas Petrou, Anastasis Kounoudes et al.
Modular Robots and Swarm Intelligence
article

Enhanced Programmability of Reconfigurable Intelligent Surfaces for Smart Cities using Application-Specific Integrated Circuits

Yorgos Stratakos, Marco A. Antoniades, Loukas Petrou, Anastasis Kounoudes, Julius Georgiou, Konstantinos Michail, Thorsten Brandt, Panos Megerditchian
article en

Abstract

Reconfigurable Intelligent Surfaces (RISs) are emerging as a key technology for enhancing wireless communications in smart city environments, offering robust, low-latency connectivity. This paper presents the design of an adaptive control system based on high-voltage, mixed-signal integrated circuits for controlling tunable RISs, tailored for wireless cellular communications in urban areas. The proposed system enables enhanced programmability by dynamically steering beams and fine-tuning signal reflections through the precise control of varactor diodes embedded within the RIS unit cells. This flexible system architecture supports real-time software reconfiguration, allowing RIS units to adapt to varying network conditions and scale seamlessly across smart city infrastructures. Simulated results on a 0.35 μm CMOS high voltage process technology demonstrate the circuit's ability to achieve low power consumption and high reconfiguration speed across a wide temperature range. The control electronics are separated from the RIS panels, this way enabling tiling of multiple surfaces for scalable, cost-effective deployment. This approach offers a promising pathway to meeting the growing demand for seamless, high-speed, and energy-efficient connectivity in next-generation smart city applications, including vehicular networks, Internet of Things, smart traffic management and smart infrastructure.

University of Cyprus (CY), SignalGeneriX (Cyprus) (CY), Toronto Metropolitan University (CA)
Research and Innovation Foundation
Sustainable cities and communities
Openalex Percentile: Top 85%
Modular Robots and Swarm Intelligence
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Enhanced Programmability of Reconfigurable Intelligent Surfaces for Smart Cities using Application-Specific Integrated Circuits — Yorgos Stratakos, Marco A. Antoniades, et al. · (2025) | TGRS Research Map | TGRS