Satellite Aerial Terrestrial Hybrid NOMA Scheme in 6G Networks: An Unsupervised Learning Approach

Karavolos, Michail H.; Tsipi, Lefteris; Bithas, Petros S.; Vouyioukas, Demosthenes; Mathiopoulos, P. Takis

doi:10.1109/6gnet54646.2022.9830368

Cited by 5 publications

(7 citation statements)

References 15 publications

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“…In emergency and public safety circumstances, UAVs can act as aBSs to supply additional capacity to hotspot locations and provide network coverage [85]. Moreover, [86] discovered that employing NOMA to intelligently integrate aBSs benefits terrestrial UEs by increasing the spectrum efficiency and system sum rate. The study of [87] focused on cellular-connected UAVs employed for surveillance, considering a trajectory-based movement in PD uplink aerial-terrestrial NOMA to enable simultaneous uplink transmissions.…”

Section: B Absmentioning

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: B Absmentioning

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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“…Finally, the seamless communication between the UFBS and the GMTs requires a reliable and efficient backhaul network. In this regard, we propose the use of zero-touch commissioning (ZTC) cloud radio access network (C-RAN) for the UAV backhaul, as it can provide efficient and automated network management [ 25 , 29 ]. The ZTC-C-RAN model comprises a control element that performs the ZTC procedures, including the instantiation, configuration, and synchronization of the UAV and D2D cooperative network as well as the placement of the UFBS in the region of interest A .…”

Section: System Modelmentioning

confidence: 99%

“…To conduct performance analysis, the channel complex coefficient for each

is denoted as

, and follows the complex Gaussian distribution with zero mean and unit variance

. Additionally, the path loss attenuation of the UFBS signal is modeled using the elevation angle-based path loss model [ 25 ] in an urban environment, and is represented as follows:

where

is the free space pathloss given by

is the transmission distance between UFBS and each

, and c is the speed of light. In addition, the

and

coefficients reflect the extra losses for LoS and Non-LoS (NLoS) air-to-ground transmission links, and they depend on the propagation environment.…”

Section: System Modelmentioning

confidence: 99%

“…As the height of the UFBS increases the elevation angle also increases, leading to an increased probability of line-of-sight, i.e., obscurance by buildings and other surrounding objects is reduced. Based on this behavior, the A2G path loss

function is convex [ 25 ]. Thus, it can be deduced that the global minimum is consistently located at the critical point which can be derived through the Equation ( 30 ).…”

Section: Ufbs Placement Proceduresmentioning

confidence: 99%

“…As a result, it leads to a more precise and reliable UAV placement. Thus conspicuously, the combination of these two algorithms exploits the strengths of both k-means and k-medoids in determining the ideal UAV placement [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning-Based Methods for Enhancement of UAV-NOMA and D2D Cooperative Networks

Tsipi

Karavolos

Bithas

et al. 2023

Sensors

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The cooperative aerial and device-to-device (D2D) networks employing non-orthogonal multiple access (NOMA) are expected to play an essential role in next-generation wireless networks. Moreover, machine learning (ML) techniques, such as artificial neural networks (ANN), can significantly enhance network performance and efficiency in fifth-generation (5G) wireless networks and beyond. This paper studies an ANN-based unmanned aerial vehicle (UAV) placement scheme to enhance an integrated UAV-D2D NOMA cooperative network.The proposed placement scheme selection (PSS) method for integrating the UAV into the cooperative network combines supervised and unsupervised ML techniques. Specifically, a supervised classification approach is employed utilizing a two-hidden layered ANN with 63 neurons evenly distributed among the layers. The output class of the ANN is utilized to determine the appropriate unsupervised learning method—either k-means or k-medoids—to be employed. This specific ANN layout has been observed to exhibit an accuracy of 94.12%, the highest accuracy among the ANN models evaluated, making it highly recommended for accurate PSS predictions in urban locations. Furthermore, the proposed cooperative scheme allows pairs of users to be simultaneously served through NOMA from the UAV, which acts as an aerial base station. At the same time, the D2D cooperative transmission for each NOMA pair is activated to improve the overall communication quality. Comparisons with conventional orthogonal multiple access (OMA) and alternative unsupervised machine-learning based-UAV-D2D NOMA cooperative networks show that significant sum rate and spectral efficiency gains can be harvested through the proposed method under varying D2D bandwidth allocations.

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Empowering Non-Terrestrial Networks With Artificial Intelligence: A Survey

Iqbal,

Tham,

Wong

et al. 2023

IEEE Access

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6G networks can support global, ubiquitous and seamless connectivity through the convergence of terrestrial and non-terrestrial networks (NTNs). Unlike terrestrial scenarios, NTNs pose unique challenges including propagation characteristics, latency and mobility, owing to the operations in spaceborne and airborne platforms. To overcome all these technical hurdles, this survey paper presents the use of artificial intelligence (AI) techniques in learning and adapting to the complex NTN environments. We begin by providing an overview of NTNs in the context of 6G, highlighting the potential security and privacy issues. Next, we review the existing AI methods adopted for 6G NTN optimization, starting from machine learning (ML), through deep learning (DL) to deep reinforcement learning (DRL). All these AI techniques have paved the way towards more intelligent network planning, resource allocation (RA), and interference management. Furthermore, we discuss the challenges and opportunities in AI-powered NTN for 6G networks. Finally, we conclude by providing insights and recommendations on the key enabling technologies for future AI-powered 6G NTNs.

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Satellite Aerial Terrestrial Hybrid NOMA Scheme in 6G Networks: An Unsupervised Learning Approach

Cited by 5 publications

References 15 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

Machine Learning-Based Methods for Enhancement of UAV-NOMA and D2D Cooperative Networks

Empowering Non-Terrestrial Networks With Artificial Intelligence: A Survey

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