NOMA in UAV-aided cellular offloading: A machine learning approach

Zhong, Ruikang; Liu, Xiao; Liu, Yuanwei; Chen, Yue

doi:10.1109/gcwkshps50303.2020.9367389

Cited by 4 publications

(6 citation statements)

References 16 publications

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“…The proposed scheme shows a significant increase in the energy efficiency of the NOMA based UAV-BSs compared to OMA. Zhong et al [39] 2020 Their findings demonstrated the superiority of the NOMA framework, and the proposed MDQN paradigm outperforms the traditional DQN paradigm in terms of convergence. Tang et al [54] 2020 They proposed a low-complexity mechanism that can converge to a near-optimal solution in three iterations in polynomial time, maximizing the number of users with satisfied QoS experiences.…”

Section: Articlesmentioning

confidence: 98%

“…On the contrary, UAV-enabled wireless networks can be used as a backup when terrestrial cellular networks relying on ground base stations (GBSs) are rendered inoperable due to natural disasters [10]. They found that UAVs can be used to substitute terrestrial infrastructures in certain cases to create temporary communication networks for information transfer and disaster relief [39].…”

Section: Agilitymentioning

confidence: 99%

“…Zhong et al [39] proposes a novel framework for cellular offloading using multiple UAVs, with the NOMA technique being used at each UAV to improve the wireless network's spectrum efficiency. They propose a NOMA-enhanced UAV-enabled cellular offloading framework in which multiple UAVs are deployed in three-dimensional space to supplement terrestrial infrastructures.…”

Section: Noma-uavmentioning

confidence: 99%

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UAV-enabled communications using NOMA for 5G and beyond: research challenges and opportunities

Kharil,

Johal,

Idris

et al. 2023

IJEECS

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Future wireless networks are expected to provide ubiquitous connectivity to a wide range of devices with varying traffic patterns, wherever and whenever it is required. Unmanned aerial vehicles (UAVs) are also considered a potential technique for accommodating massive connections and providing seamless coverage. They can be used as flying base stations (BSs) to take advantage of line-of-sight (LoS) connections and effectively support wireless communication coverage and throughput for 5G and beyond. However, the use of highly mobile and energy-constrained UAVs for wireless communications brings plenty of new challenges. 5G wireless networks require non-orthogonal multiple access (NOMA) to be able to meet heterogeneous requirements for low latency, high dependability, massive connection, better fairness, and high throughput. This paper presents an overview of NOMA-based UAV enabled communications by introducing the background of UAV communication and NOMA schemes. Power allocation schemes are also explored as they are critical controlling mechanisms for performance optimization of NOMA-UAV systems. We also categorize UAV-enabled communication applications for usage in both routine professional settings and emergency scenarios. Finally, we address several open research questions that need to be solved for NOMA, as well as new opportunities and future research trends to be exploited.

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Section: Articlesmentioning

confidence: 98%

Section: Agilitymentioning

confidence: 99%

Section: Noma-uavmentioning

confidence: 99%

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UAV-enabled communications using NOMA for 5G and beyond: research challenges and opportunities

Kharil,

Johal,

Idris

et al. 2023

IJEECS

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“…Recent research attempts have investigated the use of NOMA to enhance the performance of UAV-enabled communication systems [ 16 , 17 , 18 ]. The authors of [ 16 ] have studied a NOMA-based UAV-enabled communication network.…”

Section: Introductionmentioning

confidence: 99%

“…The numerical results have shown that the NOMA scheme outperforms OMA, in terms of achievable rate, for different system parameters. Subsequently, the authors in [ 17 ] developed a novel NOMA UAV-assisted offloading architecture for cellular networks to significantly enhance the system’s spectrum efficiency. Specifically, the 3D trajectory design and power allocation optimization problem are formulated to maximize the system sum rate.…”

Section: Introductionmentioning

confidence: 99%

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

Karavolos

Tsipi

Bithas

et al. 2022

2022 1st International Conference on 6G Networking (6GNet)

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NOMA in UAV-aided cellular offloading: A machine learning approach

Cited by 4 publications

References 16 publications

UAV-enabled communications using NOMA for 5G and beyond: research challenges and opportunities

UAV-enabled communications using NOMA for 5G and beyond: research challenges and opportunities

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

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

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