Intelligent Localization-Based DDPG for UAV-Borne RIS in Vehicular Communication
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Intelligent Localization-Based DDPG for UAV-Borne RIS in Vehicular Communication
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20203 - Telecommunications
Result continuities
Project
<a href="/en/project/GA23-05646S" target="_blank" >GA23-05646S: Intelligent Radio Resource and Mobility Management based on Federated Learning</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE Transactions on Vehicular Technology
ISSN
0018-9545
e-ISSN
1939-9359
Volume of the periodical
74
Issue of the periodical within the volume
11
Country of publishing house
US - UNITED STATES
Number of pages
6
Pages from-to
18168-18173
UT code for WoS article
001621303400012
EID of the result in the Scopus database
2-s2.0-105008020939