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المؤلفShehab, Muhammad
المؤلفCiftler, Bekir S.
المؤلفKhattab, Tamer
المؤلفAbdallah, Mohamed M.
المؤلفTrinchero, Daniele
تاريخ الإتاحة2022-10-31T19:21:54Z
تاريخ النشر2022
اسم المنشورIEEE Open Journal of the Communications Society
المصدرScopus
معرّف المصادر الموحدhttp://dx.doi.org/10.1109/OJCOMS.2022.3165590
معرّف المصادر الموحدhttp://hdl.handle.net/10576/35645
الملخصIn this work, we examine an intelligent reflecting surface (IRS) assisted downlink non-orthogonal multiple access (NOMA) scenario intending to maximize the sum-rate of users. The optimization problem at the IRS is quite complicated, and non-convex since it requires the tuning of the phase shift reflection matrix. Driven by the rising deployment of deep reinforcement learning (DRL) techniques that are capable of coping with solving non-convex optimization problems, we employ DRL to predict and optimally tune the IRS phase shift matrices. Simulation results reveal that the IRS-assisted NOMA system based on our utilized DRL scheme achieves a high sum-rate compared to OMA-based one, and as the transmit power increases, the capability of serving more users increases. Furthermore, results show that imperfect successive interference cancellation (SIC) has a deleterious impact on the data rate of users performing SIC. As the imperfection increases by ten times, the rate decreases by more than 10%. 2020 IEEE.
اللغةen
الناشرInstitute of Electrical and Electronics Engineers Inc.
الموضوع5G and beyond
6G
deep reinforcement learning (DRL)
Intelligent reflecting surfaces (IRS)
non-orthogonal multiple access (NOMA)
phase shift design
العنوانDeep Reinforcement Learning Powered IRS-Assisted Downlink NOMA
النوعArticle
الصفحات729-739
رقم المجلد3
dc.accessType Abstract Only


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