Terahertz Multiple Access: A Deep Reinforcement Learning Controlled Multihop IRS Topology

<p dir="ltr">We explore THz communication uplink multi-access with multi-hop Intelligent reflecting surfaces (IRSs) under correlated channels. Our aims are twofold: 1) enhancing the data rate of a desired user while dealing with interference from another user and 2) maximizing the co...

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محفوظ في:

التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Muhammad Shehab (16904880) (author)
مؤلفون آخرون:	Mohamed Elsayed (3524918) (author), Abdullateef Almohamad (16870074) (author), Ahmed Badawy (6992093) (author), Tamer Khattab (16870086) (author), Nizar Zorba (16888728) (author), Mazen Hasna (16904661) (author), Daniele Trinchero (16904886) (author)
منشور في:	2024
الموضوعات:	Engineering Electrical engineering Information and computing sciences Machine learning Artificial intelligence multi-access communication sub-millimeter wave communication communication system performance Optimization Reflection Wireless communication Uplink Propagation losses Correlation Array signal processing
الوسوم:	إضافة وسم لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!

Terahertz Multiple Access: A Deep Reinforcement Learning Controlled Multihop IRS Topology

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