Throughput-aware path planning for UAVs in D2D 5G networks

Shi, Lin; Jiang, Zhongyi; Xu, Sugang

doi:10.1016/j.adhoc.2021.102427

Cited by 16 publications

(5 citation statements)

References 26 publications

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“…Furthermore, in the event of natural disasters and during search and rescue operations, base stations are often shut down or overloaded, and could only provide limited services for a specific limited number of users or equipment. Hence, in emergencies, D2D- based-technology networks could provide temporary broadband services for emergency workers, doctors, and nurses to access patients' data, in addition to reporters and journalists to cover news and quickly disseminate government emergency announcements in a timely and orderly fashion [50], [66]. Moreover, unmanned aerial vehicles (UAV) were proposed as an emerging approach to deploy emergency networks in the case of damaged network infrastructure [67].…”

Section: Emergency Networkmentioning

confidence: 99%

Survey on Device to Device (D2D) Communication for 5GB/6G Networks: Concept, Applications, Challenges, and Future Directions

et al. 2022

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Device-to-device (D2D) communication is one of the most promising technologies in wireless cellular networks that can be employed to improve spectral and energy efficiency, increase data rates, and reduce links latency. This paper investigates fifth generation and beyond (5GB) networks, the basics of D2D communication, applications, and classification. Herein, D2D in in-band (IBD) and out-band (OBD) modes are discussed. This paper also presents the integration of D2D communication with other prominent technologies and demonstrates the importance of integration with possible solutions in improving network performance. We further investigate the challenges of D2D communication, opportunities, and future research directions of D2D in 5GB networks. In addition, D2D communication in 6G network challenges and open research areas are introduced.INDEX TERMS 5G, 5GB, 6G, D2D communication, millimeter wave (mm-Wave), visible light communication (VLC), ultra-dense network (UDN), and unmanned aerial vehicle (UAV).

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Section: Emergency Networkmentioning

confidence: 99%

Survey on Device to Device (D2D) Communication for 5GB/6G Networks: Concept, Applications, Challenges, and Future Directions

et al. 2022

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“…The placement of UAV-BS was modeled as an optimization problem to obtain the necessary network resources and provide temporary connectivity to improve system performance. In the work [20], a route-planning method was developed for UAVs deployed in areas with limited network coverage for applications that stream data. Unlike other planning, this requires the QoS served by the UAVs to preserve bandwidth.…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, the average propagation loss results from the altitude and the coverage radius of the UAV-BS, calculated in Equation (20):…”

Section: Set Of Cluster Centroid P Uavmentioning

confidence: 99%

Resource Management in 5G Networks Assisted by UAV Base Stations: Machine Learning for Overloaded Macrocell Prediction Based on Users’ Temporal and Spatial Flow

Alfaia

Souto

Cardoso

et al. 2022

Drones

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The rapid growth of data traffic due to the demands of new services and applications poses new challenges to the wireless network. Unmanned aerial vehicles (UAVs) can be a solution to support wireless networks during congestion, especially in scenarios where the region has high traffic peaks due to the temporal and spatial flow of users. In this paper, an intelligent machine-learning-based system is proposed to deploy UAV base stations (UAV-BS) to temporarily support the mobile network in regions suffering from the congestion effect caused by the high density of users. The system includes two main steps, the load prediction algorithm (LPA) and the UAV-BSs clustering and positioning algorithm (UCPA). In LPA, the load history generated by the mobile network is used to predict which macrocells are congested. In UCPA, planning is performed to calculate the number of UAV BSs needed based on two strategies: naïve and optimized, in addition to calculating the optimal positioning for each device requested to support the overloaded macrocells. For prediction, we used two models, generalized regression neural networks (GRNN) and random forest, and the results showed that both models were able to make accurate predictions, and the random forest model was better with an accuracy of over 85%. The results showed that the intelligent system significantly reduced the overhead of the affected macrocells, improved the quality of service (QoS), and reduced the probability of blocking users, as well as defined the preventive scheduling for the UAV BSs, which benefited the scheduling and energy efficiency.

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“…where ρ loc k marked as the chip correlation coefficient of the GME k. q , ∀k ∈ K,∀m ∈ M denotes the horizontal distance between the GME k and the UAV m, R = H tan ϑ indicates the coverage radius of each UAV, and ϑ is UAV antenna elevation angle [23]. When the GME k is in the coverage of multiple UAVs, the GME k randomly selects a UAV to offload the computational task.…”

Section: Computation Modelmentioning

confidence: 99%

Computation Offloading in Multi-UAV-Enhanced Mobile Edge Networks: A Deep Reinforcement Learning Approach

et al. 2022

Wireless Communications and Mobile Computing

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In this paper, we investigate an unmanned aerial vehicle- (UAV-) enhanced mobile edge computing network (MUEMN), where multiple UAVs are deployed as aerial edge servers to provide computing services for ground moving equipment (GME). Each GME is trained to simulate movement by a Gauss-Markov random model in this MUEMN. Under the condition of limited energy cost, UAV dynamically plans its flight position according to the movement trend of GME. Our objective is to minimize the total energy consumption of GME by jointly optimizing the offloading decisions of GME and the flight positions of UAVs. More explicitly, we model the optimization problem as a Markov decision process and achieve real-time offloading decisions via deep reinforcement learning algorithm according to the dynamic system state, where the asynchronous advantage actor-critic (A3C) framework with asynchronous characteristics is leveraged to accelerate the learning process. Finally, numerical results confirm that our proposed A3C-based offloading strategy can effectively reduce the total of energy consumption of GME and ensure the continuous operation of the GME.

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Throughput-aware path planning for UAVs in D2D 5G networks

Cited by 16 publications

References 26 publications

Survey on Device to Device (D2D) Communication for 5GB/6G Networks: Concept, Applications, Challenges, and Future Directions

Survey on Device to Device (D2D) Communication for 5GB/6G Networks: Concept, Applications, Challenges, and Future Directions

Resource Management in 5G Networks Assisted by UAV Base Stations: Machine Learning for Overloaded Macrocell Prediction Based on Users’ Temporal and Spatial Flow

Computation Offloading in Multi-UAV-Enhanced Mobile Edge Networks: A Deep Reinforcement Learning Approach

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