NOMA- and MRC-Enabled Framework in Drone-Relayed Vehicular Networks: Height/Trajectory Optimization and Performance Analysis

He, Yixin; Huang, Fanghui; Wang, Dawei; Zhang, Ruonan; Gu, Xin; Pan, Jianping

doi:10.1109/jiot.2023.3303413

Cited by 13 publications

(10 citation statements)

References 42 publications

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“…The complexity of overall algorithmic framework is analyzed theoretically. Finally, the simulation results indicate that the presented scheme is superior to the state-of-the-art works [14][15][16] and benchmark scheme in terms of the sum rate. Additionally, the convergence, complexity, and performance gap caused by iterative optimization are shown.…”

mentioning

confidence: 89%

“…Additionally, a drone-assisted urban emergency communication scenario was considered in [14], where a joint altitude and resource allocation scheme was proposed to ensure information forwarding from the disaster area. Currently, in order to make full use of the advantage of drone-assisted PDEComs, combining the NOMA technique with the relaying drone has attracted much attention from researchers [15,16,20,21]. The authors in [15,21] incorporated the NOMA technique with the decode-and-forward (DF) relay protocol to improve the spectrum utilization and transmission performance of PDEComs.…”

Section: Related Workmentioning

confidence: 99%

“…Currently, in order to make full use of the advantage of drone-assisted PDEComs, combining the NOMA technique with the relaying drone has attracted much attention from researchers [15,16,20,21]. The authors in [15,21] incorporated the NOMA technique with the decode-and-forward (DF) relay protocol to improve the spectrum utilization and transmission performance of PDEComs. Furthermore, by optimally allocating the multi-dimensional resources (e.g., power and time slot scheduling), the energy-efficiency optimization problem was studied in [16,20].…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Post-Disaster Emergency Communications Enhanced by Drones and Non-Orthogonal Multiple Access: Three-Dimensional Deployment Optimization and Spectrum Allocation

Li,

Zhu,

Huang

et al. 2024

Drones

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Integrating the relaying drone and non-orthogonal multiple access (NOMA) technique into post-disaster emergency communications (PDEComs) is a promising way to accomplish efficient network recovery. Motivated by the above, by optimizing the drone three-dimensional (3D) deployment optimization and spectrum allocation, this paper investigates a quality of service (QoS)-driven sum rate maximization problem for drone-and-NOMA-enhanced PDEComs that aims to improve the data rate of cell edge users (CEUs). Due to the non-deterministic polynomial (NP)-hard characteristics, we first decouple the formulated problem. Next, we obtain the optimal 3D deployment with the aid of a long short-term memory (LSTM)-based recurrent neural network (RNN). Then, we transform the spectrum allocation problem into an optimal matching issue, based on which the Hungarian algorithm is employed to solve it. Finally, the simulation results show that the presented scheme has a significant performance improvement in the sum rate compared with the state-of-the-art works and benchmark scheme. For instance, by adopting the NOMA technique, the sum rate can be increased by 9.72% and the needs of CEUs can be satisfied by enabling the relaying drone. Additionally, the convergence, complexity, and performance gap caused by iterative optimization are discussed and analyzed.

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mentioning

confidence: 89%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Post-Disaster Emergency Communications Enhanced by Drones and Non-Orthogonal Multiple Access: Three-Dimensional Deployment Optimization and Spectrum Allocation

Li,

Zhu,

Huang

et al. 2024

Drones

Self Cite

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“…In scenarios where ground-based communication infrastructure may be compromised due to natural disasters or accidents, UAVs serve as agile alternatives, ensuring uninterrupted communication and operational continuity. This redundancy enhances the reliability of IoV networks, fostering safer and more efficient transportation ecosystems even in the face of unforeseen disruptions [8][9][10]. In essence, by harnessing the capabilities of UAVs as active components within IoV, rural highways stand to benefit from enhanced connectivity, resilience, and efficiency, paving the way for smarter and more sustainable transportation solutions.…”

Section: Introductionmentioning

confidence: 99%

Joint Phase Shift Design and Resource Management for a Non-Orthogonal Multiple Access-Enhanced Internet of Vehicle Assisted by an Intelligent Reflecting Surface-Equipped Unmanned Aerial Vehicle

Wang,

He,

Chen

et al. 2024

Drones

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This paper integrates intelligent reflecting surfaces (IRS) with unmanned aerial vehicles (UAV) to enhance the transmission performance of the Internet of Vehicles (IoV) through non-orthogonal multiple access (NOMA). It focuses on strengthening the signals from cell edge vehicles (CEVs) to the base station by optimizing the wireless propagation environment via an IRS-equipped UAV. The primary goal is to maximize the sum data rate of CEVs while satisfying the constraint of the successive interference cancellation (SIC) decoding threshold. The challenge lies in the non-convex nature of jointly considering the power control, subcarrier allocation, and phase shift design, making the problem difficult to optimally solve. To address this, the problem is decomposed into two independent subproblems, which are then solved iteratively. Specifically, the optimal phase shift design is achieved using the deep deterministic policy gradient (DDPG) algorithm. Furthermore, the graph theory is applied to determine the subcarrier allocation policy and derive a closed-form solution for optimal power control. Finally, the simulation results show that the proposed joint phase shift and resource management scheme significantly enhances the sum data rate compared to the state-of-the-art schemes, thereby demonstrating the benefits of integrating the IRS-equipped UAV into NOMA-enhanced IoV.

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“…In recent years, studies of MCS task allocation have become a hotspot in academia, and many novel strategies have been proposed, with the purpose of maximizing sensing quality with a limited budget [ 23 , 24 , 25 ] or minimizing incentive costs while ensuring a minimal degree of sensing quality loss [ 26 , 27 ]. However, under the diversified dynamic MCS environment, particularly in SAGSINs, allocating large-scale tasks to mobile users according to heterogeneous sensors (e.g., mobile users, UAVs [ 28 ], ships, and stationary sensors) to achieve the tradeoff between the sensing quality and sensing cost is a huge challenge.…”

Section: Introductionmentioning

confidence: 99%

UAV-Assisted Cluster-Based Task Allocation for Mobile Crowdsensing in a Space–Air–Ground–Sea Integrated Network

Liu,

Li,

Cheng

et al. 2023

Sensors

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Mobile crowdsensing (MCS), which is a grassroots sensing paradigm that utilizes the idea of crowdsourcing, has attracted the attention of academics. More and more researchers have devoted themselves to adopting MCS in space–air–ground–sea integrated networks (SAGSINs). Given the dynamics of the environmental conditions in SAGSINs and the uncertainty of the sensing capabilities of mobile people, the quality and coverage of the sensed data change periodically. To address this issue, we propose a novel UAV-assisted cluster-based task allocation (UCTA) algorithm for MCS in SAGSINs in a two-stage process. We first introduce the edge nodes and establish a three-layer hierarchical system with UAV-assistance, called “Platform–Edge Cluster–Participants”. Moreover, an edge-aided attribute-based cluster algorithm is designed, aiming at organizing tasks into clusters, which significantly diminishes both the communication overhead and computational complexity while enhancing the efficiency of task allocation. Subsequently, a greedy selection algorithm is proposed to select the final combination that performs the sensing task in each cluster. Extensive simulations are conducted comparing the developed algorithm with the other three benchmark algorithms, and the experimental results unequivocally endorse the superiority of our proposed UCTA algorithm.

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NOMA- and MRC-Enabled Framework in Drone-Relayed Vehicular Networks: Height/Trajectory Optimization and Performance Analysis

Cited by 13 publications

References 42 publications

Post-Disaster Emergency Communications Enhanced by Drones and Non-Orthogonal Multiple Access: Three-Dimensional Deployment Optimization and Spectrum Allocation

Post-Disaster Emergency Communications Enhanced by Drones and Non-Orthogonal Multiple Access: Three-Dimensional Deployment Optimization and Spectrum Allocation

Joint Phase Shift Design and Resource Management for a Non-Orthogonal Multiple Access-Enhanced Internet of Vehicle Assisted by an Intelligent Reflecting Surface-Equipped Unmanned Aerial Vehicle

UAV-Assisted Cluster-Based Task Allocation for Mobile Crowdsensing in a Space–Air–Ground–Sea Integrated Network

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