Joint Beamforming and Resource Allocation for STAR-IRS-Aided SCMA ISAC Systems Using Meta Deep Reinforcement Learning

Armin Farhadi; Ali Olfat; Christos Masouros

doi:10.1109/twc.2025.3580347

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Joint Beamforming and Resource Allocation for STAR-IRS-Aided SCMA ISAC Systems Using Meta Deep Reinforcement Learning

Armin Farhadi Ali Olfat Christos Masouros

Year: 2025 Journal: IEEE Transactions on Wireless Communications Vol: 24 (12)Pages: 10534-10550 Publisher: Institute of Electrical and Electronics Engineers

DOI: 10.1109/twc.2025.3580347

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Keywords:

Reinforcement learning Computer science Joint (building) Resource allocation Beamforming Telecommunications Distributed computing Artificial intelligence Computer network Engineering

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Advanced Wireless Communication Technologies

Physical Sciences → Engineering → Electrical and Electronic Engineering

Optical Wireless Communication Technologies

Physical Sciences → Engineering → Electrical and Electronic Engineering

Optical and Acousto-Optic Technologies

Physical Sciences → Physics and Astronomy → Atomic and Molecular Physics, and Optics

Joint Beamforming and Resource Allocation for STAR-IRS-Aided SCMA ISAC Systems Using Meta Deep Reinforcement Learning

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