[en] The next generation of wireless communication networks
is expected to utilise unmanned aerial vehicles (UAVs) and
reconfigurable intelligent surfaces (RISs) to enhance spectrum
and energy efficiency. This work establishes a theoretical foundation
for RIS-assisted UAV implementation, capitalising on the
passive beamforming capabilities of RIS alongside the adaptable
deployment and dynamic mobility of UAVs to enhance internetof-
things (IoT) network performance. A comprehensive framework
for RIS-assisted UAV IoT data collection is represented
and optimised to enhance critical performance metrics, including
the quantity of served IoT devices and achievable data rates.
This framework is instrumental in urban IoT networks, such
as smart cities, where blockages and fading channels hinder
reliable communication. The optimisation strategy deploys a
deep reinforcement learning (DRL) algorithm to fine-tune UAV
trajectories and IoT device scheduling decisions, complemented
by a codebook for RIS beamforming to optimise the RIS
phase shift matrix. This integrated approach addresses the everincreasing
demand for efficient data collection in wireless IoT
networks, providing a scalable and reliable solution for efficient
data collection under dynamic urban environments channel
conditions. Simulation results show substantial improvements
in system performance, demonstrating the efficiency of the
proposed algorithm. By coordinating the RIS phase shift matrix
and UAV trajectory planning, the proposed framework achieves
improvements in terms of the number of served IoT devices
and achievable data rates. For example, compared to baseline
methods, our approach outperforms benchmark scenarios by
over 50% in terms of the number of served devices. The results
reveal the potential of RIS-assisted UAV solutions in meeting the
increasing demands of wireless IoT networks.
Disciplines :
Computer science
Author, co-author :
Abualhayja'a, Mohammad; School of Electrical, Electronic and Mechanical Engineering, University of Bristol, Bristol, U.K.
Centeno, Anthony; James Watt School of Engineering, University of Glasgow, Glasgow, UK
TRAN DINH, Hieu ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom
Butt, M. Majid; Nokia, 2000 Lucent Lane, Naperville, IL, US
Sehier, Philippe; Nokia Standards, 12 Rue Jean Bart, Massy, France
Imran, Muhammad Ali; James Watt School of Engineering, University of Glasgow, Glasgow, UK
Mohjazi, Lina; James Watt School of Engineering, University of Glasgow, Glasgow, UK
External co-authors :
yes
Language :
English
Title :
Efficient Data Harvesting in Urban IoT Networks: DRL for RIS-UAV Communications
Publication date :
11 September 2025
Journal title :
IEEE Transactions on Vehicular Technology
ISSN :
0018-9545
Publisher :
Institute of Electrical and Electronics Engineers (IEEE)
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