Outage Probability Minimization in Secure NOMA Cognitive Radio Systems With UAV Relay: A Machine Learning Approach

Vo, Van Nhan; Nguyen, Long D.; Tran, Hung; Dang, Viet-Hung; Niyato, Dusit; Cuong, Dang Ngoc; Luong, Nguyen Cong; So-In, Chakchai

doi:10.1109/tccn.2022.3226184

Cited by 9 publications

(1 citation statement)

References 47 publications

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“…In addition, [81] showed that D2D cooperative transmission pairs of users are simultaneously served through NOMA from the UAV, which acts as an aerial base station to improve overall communication quality. Furthermore, [82] employed ML to optimize a MIMO NOMA system with a UAV as a relay by optimizing the transmission power, UAV coordinates, and power allocation.…”

Section: A Uav As Relaymentioning

confidence: 99%

A Survey on Non-Orthogonal Multiple Access for Unmanned Aerial Vehicle Networks: Machine Learning Approach

Mayarakaca,

Lee

2024

IEEE Access

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The rapid evolution of wireless communication has affected unmanned aerial vehicles (UAV), which are expected to be used in diverse applications in smart cities, military operations, and cellular networks. To address the significant impacts of rapid wireless communication advancements, along with the escalating demand for user equipment (UE), multiple access technique approaches, such as non-orthogonal multiple access (NOMA), have been proposed. NOMA has the key distinguishing feature of supporting more UE, particularly UAV-enabled communication networks. In these networks, to support more UE, multiple UE share the same frequency resources, which can be beneficial for UAV communication networks. To achieve optimal results, UAVs need to adaptively adjust to their environment, and this can be achieved through employing machine learning (ML). ML is a subfield of artificial intelligence (AI) that enables computers to learn from data and make predictions or decisions without being explicitly programmed. Employing ML can aid NOMA for UAV networks to analyze various parameters and rely on accurate predictions regarding various aspects of communication, such as channel conditions, user mobility, and traffic demands. The use of ML in NOMA-UAV networks can lead to significant improvements in wireless communication systems. In this paper, we present a survey on the potential of NOMA techniques applied to UAVs using ML methods to enhance UAVs in wireless communication networks. Specifically, a basic overview of UAV and NOMA will first be introduced. The role of NOMA in UAV networks is then divided into two categories: the principles and applications of NOMA in UAV networks. Finally, implement ML on NOMA for UAVs by representing the diverse applications of ML systems. In addition, we highlight several open research problems as possible directions for future research.INDEX TERMS Aerial networks, machine learning (ML), non-orthogonal multiple Access (NOMA), unmanned aerial vehicles (UAV).

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Section: A Uav As Relaymentioning

confidence: 99%

A Survey on Non-Orthogonal Multiple Access for Unmanned Aerial Vehicle Networks: Machine Learning Approach

Mayarakaca,

Lee

2024

IEEE Access

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Secure Conversation: A View from Physical Layer

Tran,

Hung,

et al. 2024

Lecture Notes in Computer Science

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Deep Learning for Outage Probability Minimization in Secure NOMA Energy Harvesting UAV IoT Networks

Long,

Dang,

Nguyen

et al. 2023

Mobile Netw Appl

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In this paper, a secure uplink non-orthogonal multiple access (NOMA) internet of things (IoT) system using an energy harvesting unmanned aerial vehicle (UAV) is studied. The communication protocol includes three phases: The first phase is an energy harvesting phase in which the IoT devices and UAV relay harvest radio frequency energy from a power beacon. The second phase is information transmission from IoT devices to a UAV relay. The third phase is relaying transmission from the UAV relay to the base station. Furthermore, a UAV eavesdropper (UE) wiretaps the signals from the IoT devices and UAV relay in the second and third phases. For this system, we derived the closed-form outage and intercept probabilities to evaluate the system and secrecy performances. The constrained optimization algorithm for minimizing the outage probability is then deployed to obtain training data. Finally, a deep learning model is trained to predict the optimal configuration parameters, enabling rapid adaptation to environmental conditions.

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Outage Probability Minimization in Secure NOMA Cognitive Radio Systems With UAV Relay: A Machine Learning Approach

Cited by 9 publications

References 47 publications

A Survey on Non-Orthogonal Multiple Access for Unmanned Aerial Vehicle Networks: Machine Learning Approach

A Survey on Non-Orthogonal Multiple Access for Unmanned Aerial Vehicle Networks: Machine Learning Approach

Secure Conversation: A View from Physical Layer

Deep Learning for Outage Probability Minimization in Secure NOMA Energy Harvesting UAV IoT Networks

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