Reconfigurable Intelligent Surface-Aided Cognitive NOMA Networks: Performance Analysis and Deep Learning Evaluation

This paper investigates reconfigurable intelligent surface (RIS)-aided cognitive non-orthogonal multiple access (NOMA) systems, where an RIS is deployed to serve two users under multi-primary users' constraints. Our analysis assumes imperfect channel state information and successive interferenc...

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Published inIEEE transactions on wireless communications Vol. 21; no. 12; pp. 10662 - 10677
Main Authors Vu, Thai-Hoc, Nguyen, Toan-Van, Costa, Daniel Benevides da, Kim, Sunghwan
Format Journal Article
LanguageEnglish
Published New York IEEE 01.12.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Array signal processing
Codes
Cognitive radio
Deep learning
Interference cancellation
Line of sight
Mathematical analysis
NOMA
non-orthogonal multiple access (NOMA)
Nonorthogonal multiple access
Optimization
performance analysis
Quality of service
reconfigurable intelligent surface (RIS)
Reconfigurable intelligent surfaces
Resource management
Throughput
throughput optimization
Upper bounds
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Abstract This paper investigates reconfigurable intelligent surface (RIS)-aided cognitive non-orthogonal multiple access (NOMA) systems, where an RIS is deployed to serve two users under multi-primary users' constraints. Our analysis assumes imperfect channel state information and successive interference cancellation under scenarios with and without line-of-sight (LoS) link between source and users. We derive exact closed-form expressions for the outage probability, throughput, and an upper bound for the ergodic capacity (EC). To provide further insights, an asymptotic analysis is carried out by considering two power settings at the source. It is also determined the optimal data rate factors of all users that maximize the system throughput. In addition, a deep learning framework (DLF) for EC prediction is designed. Numerical results show that: i) compared to the system without LoS link, the performance of the proposed system with LoS link can significantly improve when the number of reflecting elements at the RIS increases, and ii) the proposed system has superior performance compared to its orthogonal multiple access counterpart. Furthermore, our proposed DLF exhibits the lowest root-mean-square error and low execution-time among other approaches, verifying the effectiveness of this method for future analysis.
AbstractList This paper investigates reconfigurable intelligent surface (RIS)-aided cognitive non-orthogonal multiple access (NOMA) systems, where an RIS is deployed to serve two users under multi-primary users’ constraints. Our analysis assumes imperfect channel state information and successive interference cancellation under scenarios with and without line-of-sight (LoS) link between source and users. We derive exact closed-form expressions for the outage probability, throughput, and an upper bound for the ergodic capacity (EC). To provide further insights, an asymptotic analysis is carried out by considering two power settings at the source. It is also determined the optimal data rate factors of all users that maximize the system throughput. In addition, a deep learning framework (DLF) for EC prediction is designed. Numerical results show that: i) compared to the system without LoS link, the performance of the proposed system with LoS link can significantly improve when the number of reflecting elements at the RIS increases, and ii) the proposed system has superior performance compared to its orthogonal multiple access counterpart. Furthermore, our proposed DLF exhibits the lowest root-mean-square error and low execution-time among other approaches, verifying the effectiveness of this method for future analysis.
Author Vu, Thai-Hoc
Costa, Daniel Benevides da
Kim, Sunghwan
Nguyen, Toan-Van
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  organization: Department of Electrical, Electronic and Computer Engineering, University of Ulsan, Ulsan, Republic of Korea
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Snippet This paper investigates reconfigurable intelligent surface (RIS)-aided cognitive non-orthogonal multiple access (NOMA) systems, where an RIS is deployed to...
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SubjectTerms Array signal processing
Codes
Cognitive radio
Deep learning
Interference cancellation
Line of sight
Mathematical analysis
NOMA
non-orthogonal multiple access (NOMA)
Nonorthogonal multiple access
Optimization
performance analysis
Quality of service
reconfigurable intelligent surface (RIS)
Reconfigurable intelligent surfaces
Resource management
Throughput
throughput optimization
Upper bounds
Title Reconfigurable Intelligent Surface-Aided Cognitive NOMA Networks: Performance Analysis and Deep Learning Evaluation
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