Work

Digital Twin and Active STAR-RIS Integration for Improved URLLC in Cognitive Radio Networks

Year: 2025

Type: conference-paper

Abstract: This paper introduces a novel integration of Digital Twin (DT) and Active STAR-RIS to enhance URLLC in cognitive radio networks (CRNs). We investigate the uplink data transmission from single-antenna IoT URLLC nodes (UNs) to a multi-antenna base station (BS) facilitated by an active STAR-RIS, focusing on reducing end-to-end latency. An efficient alternating optimization (AO) algorithm is proposed to address the non-convex problem, breaking it into five sub-problems: transmit beamforming, caching and offloading policies, joint communication and computation optimization, and active STARRIS beamforming enhancement. Results show that increasing RIS elements significantly reduces latency through improved signal management and reduced interference. Active STAR-RIS with optimal beamforming outperforms passive and random strategies by dynamically adapting to channel conditions. Higher transmit power and energy levels further decrease latency, with larger data sizes prioritized in resource allocation. The findings highlight the critical role of active STAR-RIS and optimized energy and power levels in minimizing latency and enhancing system performance in CRNs.

Source: International Conference on Communications

DOI: 10.1109/icc52391.2025.11161644

Authors: Sravani Kurma, Tri Ayu Lestari, Keshav Singh, Anal Paul, Sudip Biswas

Institutions: National Sun Yat-sen University, Indian Institute of Information Technology Guwahati

Language: English

Topic: Advanced Wireless Communication Technologies

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Funder: National Science and Technology Council