Aerial Reconfigurable Intelligent Surface-Aided Wireless Communication Systems

Nhu, Tri; Kaddoum, Georges; Nguyen, Thanh-Luan; Costa, Daniel Benevides da; Haas, Zygmunt J.

doi:10.1109/pimrc50174.2021.9569450

Cited by 25 publications

(29 citation statements)

References 13 publications

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“…where β a = β 0 d −ν a , a ∈ {SR r , R r D gu } denotes the largescale average channel power gain with r = 1, • • • , K, in which β 0 denotes the average channel power gain at the reference distance d 0 = 1 m, d a denotes the distance between S → R r and R r → D gu , and ν denotes the pathloss exponent. W a denotes the small-scale fading, modeled by the Nakagami-µ distribution, as in [17]. Without loss of generality, the complex channel gain matrices W SRr and W RrDgu can be expressed as…”

Section: Airs-noma Network: System Modelmentioning

confidence: 99%

“…By considering a more realistic modeling channel, where communication systems were characterized by Nakagami-m small-scale fading and inverse-Gamma large scale shadowing, in [17] the authors investigated the delay-limited performance and the outage probability. On the other hand, the compatibility and feasibility of AIRS in a cell-free massive multiple-input multiple-output (MIMO) network to maximize the user's achievable rate were investigated in [18].…”

Section: Introductionmentioning

confidence: 99%

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Aerial Intelligent Reflecting Surfaces in MIMO-NOMA Networks: Fundamentals, Potential Achievements, and Challenges

Lima

Sena

Dinis

et al. 2022

IEEE Open J. Commun. Soc.

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Non-orthogonal multiple access (NOMA) technique and unmanned aerial vehicles (UAVs) have been recognized as promising technologies for enabling the stringent requirements of the different network infrastructures expected for the next generation of wireless networks. In parallel, intelligent reconfigurable surfaces (IRSs) have been widely pointed out as an auspicious solution to further improve spectral efficiency, coverage range, and connectivity. By integrating IRS with UAV and NOMA schemes with multiple-input multiple-output (MIMO) it is possible to smartly improve the overall network performance. In order to explore some of these potentials, this paper provides a comprehensive discussion about the interplay of aerial IRS in MIMO-NOMA (AIRS-NOMA) networks, as well its architecture, functionality principles, and performance gains. In particular, attractive gains related to the data rate maximization, user fairness, energy efficiency, and coverage range are highlighted. Simulation results are provided to support our insightful discussions, in which it is revealed that the performance gains of AIRS-NOMA networks are superior when compared to terrestrial deployment. In addition, to guide new studies perspectives, it is addressed some issues and research opportunities associated with this potential integration.

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Section: Airs-noma Network: System Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Aerial Intelligent Reflecting Surfaces in MIMO-NOMA Networks: Fundamentals, Potential Achievements, and Challenges

Lima

Sena

Dinis

et al. 2022

IEEE Open J. Commun. Soc.

View full text Add to dashboard Cite

show abstract

“…In this section, our main goal is to leveraging DNN-based approach which is a feed-forward neural network [48]. In Fig.…”

Section: B Dnn-based Secure Performance Prediction: Structurementioning

confidence: 99%

“…Then, we deploy a Monte-Carlo simulation running with coefficients in the feature vector t[i] to produce a corresponding value of secure metric, i.e. SOP P re,i [48]. In this case, 10 5 samples are processed, i.e., Data[i], i = 1, ..., 10 5 .…”

Section: Dnn: Data Setmentioning

confidence: 99%

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A Deep Neural Network for Physical Layer Security Analysis in NOMA Reconfigurable Intelligent Surfaces-Aided IoT Systems

Do¹,

Le²,

Vahid³

et al. 2022

Preprint

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<p>We focus on the secure performance metrics at the legitimate users, i.e. secure outage probability (SOP) and secrecy capacity, to quantify the secrecy performance of NOMA-RIS-aided IoT systems. We assume the RIS is placed between the access point and the legitimate devices, and is expected to enhance the link security through the smart phase shift mechanism of metasurface elements in RIS. We first present analytical results for the SOP and secrecy capacity. Next, an iterative search algorithm is adopted to exhibit optimal SOP for further insights and analysis. In order to help the base station allocate power coefficients to NOMA users properly, an efficient deep-neural network (DNN)-based secure metric prediction scheme is adopted to achieve the secure performance. Our derivations and simulations indicate that the number of meta-surface elements of the RIS and the average signal-to-noise ratio at the base station contribute the most to the system performance enhancement. </p>

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Improving the Capacity of NOMA Network Using Multiple Aerial Intelligent Reflecting Surfaces

Phu,

Nguyen,

Voznak

et al. 2023

IEEE Access

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In this paper, we propose to utilize multiple aerial intelligent reflecting surfaces (AIRSs) for significantly enhancing the ergodic capacity of a non-orthogonal multiple access (NOMA) network. Unlike previous works where only one AIRS was often utilized to support only two NOMA users, Q AIRSs and L users are applied in the proposed NOMA-AIRS network. We mathematically derive the ergodic capacity (EC) expressions at the multiple users of the proposed NOMA-AIRS network over Nakagami-m channels recommended for use in the fifth and beyond generations (5G & B5G) of mobile communications. Numerical results show that the proposed NOMA-AIRS network can deeply exploit the advantages of AIRSs. In particular, by using Q = 4 AIRSs where each AIRS has 20 reflecting elements (REs), the ECs of the proposed NOMA-AIRS network are significantly higher than the ECs of the conventional NOMA network (without AIRSs). Moreover, the ECs of the proposed NOMA-AIRS network can be two times higher than those of the conventional NOMA network in the low transmit power regime. However, if the transmit power is high enough, the benefits of AIRSs are reduced due to the NOMA features. Besides confirming the advantages of AIRSs, the effects of key parameters such as the number of REs and total of REs in the AIRSs, bandwidth, altitudes of AIRSs, and carrier frequency are thoroughly determined. Based on the achieved observations, we give some recommendations to enhance the EC performance of the proposed NOMA-AIRS network in practice scenarios.INDEX TERMS Non-orthogonal multiple access, aerial intelligent reflecting surface, multiuser systems, moment functions, ergodic capacity.

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Aerial Reconfigurable Intelligent Surface-Aided Wireless Communication Systems

Cited by 25 publications

References 13 publications

Aerial Intelligent Reflecting Surfaces in MIMO-NOMA Networks: Fundamentals, Potential Achievements, and Challenges

Aerial Intelligent Reflecting Surfaces in MIMO-NOMA Networks: Fundamentals, Potential Achievements, and Challenges

A Deep Neural Network for Physical Layer Security Analysis in NOMA Reconfigurable Intelligent Surfaces-Aided IoT Systems

Improving the Capacity of NOMA Network Using Multiple Aerial Intelligent Reflecting Surfaces

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