Intelligent Localization-Based DDPG for UAV-Borne RIS in Vehicular Communication

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F25%3A00389273" target="_blank" >RIV/68407700:21230/25:00389273 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1109/TVT.2025.3578644" target="_blank" >https://doi.org/10.1109/TVT.2025.3578644</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TVT.2025.3578644" target="_blank" >10.1109/TVT.2025.3578644</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Intelligent Localization-Based DDPG for UAV-Borne RIS in Vehicular Communication

Popis výsledku v původním jazyce

We develop a framework to simultaneously optimize the artificial noise (AN) covariance of eavesdroppers, the high-dimensional continuous phase shifts of Unmanned Aerial Vehicles (UAV)-borne reconfigurable intelligent surfaces (RISs), and beamforming of base stations (BSs) in vehicular networks. This optimization aims to maximize the worst-case secrecy rate for vehicular users (VUs) amidst eavesdroppers considering inaccurate channel state information (CSI) and VUs' quality-of-service (QoS) requirements. We propose a predictive localization-based actor-critic deep reinforcement learning (DRL) solution. We employ a deep neural network (DNN) to predict the future spatial location of VUs and eavesdroppers, since these are apriory unknown. Then, the predicted positions are fed to a Deep Deterministic Policy Gradient (DDPG) algorithm, which determines the AN matrix and RIS beamforming vector. The DDPG integrated with DNN-based predictive localization offers a significant advantage over the standard DDPG, as additional states are incorporated into the system, thereby enhancing the model's capability to coordinate DDPG based on predicted location and to capture more complex and abstract features of the highly dynamic vehicular network. Simulations demonstrate the proposed DDPG with predictive localization ensures secure and reliable communications, surpassing state-of-the-art baselines.

Název v anglickém jazyce

Intelligent Localization-Based DDPG for UAV-Borne RIS in Vehicular Communication

Popis výsledku anglicky

We develop a framework to simultaneously optimize the artificial noise (AN) covariance of eavesdroppers, the high-dimensional continuous phase shifts of Unmanned Aerial Vehicles (UAV)-borne reconfigurable intelligent surfaces (RISs), and beamforming of base stations (BSs) in vehicular networks. This optimization aims to maximize the worst-case secrecy rate for vehicular users (VUs) amidst eavesdroppers considering inaccurate channel state information (CSI) and VUs' quality-of-service (QoS) requirements. We propose a predictive localization-based actor-critic deep reinforcement learning (DRL) solution. We employ a deep neural network (DNN) to predict the future spatial location of VUs and eavesdroppers, since these are apriory unknown. Then, the predicted positions are fed to a Deep Deterministic Policy Gradient (DDPG) algorithm, which determines the AN matrix and RIS beamforming vector. The DDPG integrated with DNN-based predictive localization offers a significant advantage over the standard DDPG, as additional states are incorporated into the system, thereby enhancing the model's capability to coordinate DDPG based on predicted location and to capture more complex and abstract features of the highly dynamic vehicular network. Simulations demonstrate the proposed DDPG with predictive localization ensures secure and reliable communications, surpassing state-of-the-art baselines.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20203 - Telecommunications

Projekt

<a href="/cs/project/GA23-05646S" target="_blank" >GA23-05646S: Inteligentní přidělovaní rádiových prostředků a řízení mobility založené na federovaném učení</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

IEEE Transactions on Vehicular Technology

ISSN

0018-9545

e-ISSN

1939-9359

Svazek periodika

74

Číslo periodika v rámci svazku

11

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

6

Strana od-do

18168-18173

Kód UT WoS článku

001621303400012

EID výsledku v databázi Scopus

2-s2.0-105008020939