Citation

Benfarhat, Ikram and Sheraz, Muhammad and Munir, Mehr E and Chuah, Teong Chee and Lee, It Ee and Yeoh, Chun Yeow and Danyal, Muhammad (2025) A Reinforcement Learning-Driven STAR-RIS-Assisted UAV for Integrated Sensing, Communication, and Computation. In: 101st IEEE Vehicular Technology Conference, VTC 2025-Spring 2025, 17 June 2025 - 20 June 2025, Oslo, Norway.

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Abstract

In this paper, we present an integrated sensing, communication, and computing (ISCC) framework tailored for unmanned aerial vehicle (UAV) systems, aimed at preprocessing radar-sensed data before offloading it to the base station (BS) for computation. To enhance full-space coverage capabilities, we use the simultaneously transmitting and reflecting surface reconfigurable intelligent surfaces (STAR-RIS) technology, which empowers the ISCC network by enabling dynamic control over signal transmission and reflection. The proposed framework achieves optimal weighted sum-rate performance while satisfying fundamental sensing requirements through joint optimization of ISCC beamforming, subcarrier allocation, and STAR-RIS-based transmission and reflection beamforming. The Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm is implemented and evaluated against baseline schemes, demonstrating improved performance.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Beam-forming, ISCC, radar sensing, reinforcement learning, STAR-RIS, TD3, UAV
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800-8360 Electronics > TK7871 Electronics--Materials
Divisions:	Faculty of Artificial Intelligence & Engineering (FAIE)
Depositing User:	Nor Afiqah Mohd Adnan
Date Deposited:	06 Nov 2025 02:56
Last Modified:	06 Nov 2025 14:17
URII:	http://shdl.mmu.edu.my/id/eprint/14701

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