Energy-Efficient Multi-UAV-Enabled Multiaccess Edge Computing Incorporating NOMA

Zhang, Xiaochen; Zhang, Jiao; Xiong, Jun; Zhou, Li; Wei, Jibo

doi:10.1109/jiot.2020.2980035

Cited by 135 publications

(41 citation statements)

References 49 publications

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“…proposed a multi-UAV-assisted multi-access edge computing scheme in-tegrated with NOMA to solve the high-energy-consumption and computation capacity problems. Additionally, they used SCA and quadratic approximation techniques to evaluate the optimal problems and formulated an algorithm for obtaining an optimal solution [20].…”

Section: A Prior Studiesmentioning

confidence: 99%

Cognitive UAV-Aided URLLC and mMTC Services: Analyzing Energy Efficiency and Latency

et al. 2021

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Section: A Prior Studiesmentioning

confidence: 99%

Cognitive UAV-Aided URLLC and mMTC Services: Analyzing Energy Efficiency and Latency

et al. 2021

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“…In general, the A2G channel includes both line-of-sight (LOS) and non-line-ofsight (NLOS) paths, due to the ground reflection, building scattering, and so on. It is still a challenge to model the complex effects, and the probabilistic LOS model is used in [24] [25]. In contrast, the A2A channel with few obstacles has higher probability of LOS link, but larger Doppler effect caused by higher relative velocity.…”

Section: System Model and Problem Formulation A System Modelmentioning

confidence: 99%

Secrecy Energy Efficiency Maximization for UAV Swarm Assisted Multi-Hop Relay System: Joint Trajectory Design and Power Control

Jin

Zheng

et al. 2021

IEEE Access

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Unmanned aerial vehicle (UAV) relay can effectively improve the coverage and performance of land communications, which makes it prospective to be deployed in various applications. However, there exist some practical challenges we have to face in UAV-enabled communication system designing, such as secrecy and wiretapping threats due to the air-to-ground line-of-sight communication link, and energy consumption issues because of the limitation in energy-constraint. To deal with these two key issues, we develop a novel UAV swarm assisted multi-hop mobile relay system which can be applied in special scenarios with severe blockage or long distance to enhance communication performance. An effective cooperative transmission scheme is proposed to address the security issues in a physical layer security perspective, where several UAVs serve as multi-hop relays forwarding information between ground users and the other UAVs are employed as friendly jammers confusing the ground eavesdroppers. The minimum secrecy energy efficiency (MSEE), namely the minimum achievable secrecy rate per energy consumption unit over legitimate links, is designed as the performance indicator to quantify the secure and energyefficient transmission. Subject to information-causality, transmit power, and mobility constraints, the UAV swarm assisted MSEE maximization transmission scheme is formulated into a complicated non-convex problem. To solve the non-convex fractional optimization problem, we decouple the original problem into sub-problems and further propose an efficient algorithm by applying the block coordinate descent method, successive convex approximation (SCA) techniques, and Dinkelbach method. Numerical results demonstrate that the proposed scheme achieves considerable performance enhancement on MSEE compared with benchmark schemes.

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“…, where H represents the flying height of UAVs. To simplify the model, it is assumed that all UAVs are deployed at the same height and their positions remain unchanged after deployment in a certain time slot [31].…”

Section: Communication Modelmentioning

confidence: 99%

Joint computation offloading and deployment optimization in multi-UAV-enabled MEC systems

Chen

Zheng

Zhang

et al. 2021

Peer-to-Peer Netw. Appl.

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The combination of unmanned aerial vehicles (UAVs) and mobile edge computing (MEC) technology breaks through the limitations of traditional terrestrial communications. The effective line-of-sight channel provided by UAVs can greatly improve the communication quality between edge servers and mobile devices (MDs). To further enhance the Quality-of-Service (QoS) of MEC systems, a multi-UAV-enabled MEC system model is designed. In the proposed model, UAVs are regarded as edge servers to offer computing services for MDs, aiming to minimize the average task response time by jointly optimizing UAV deployment and computation offloading. Based on the problem definition, a two-layer joint optimization method (PSO-GA-G) is proposed. First, the outer layer utilizes a Particle Swarm Optimization algorithm combined with Genetic Algorithm operators (PSO-GA) to optimize UAV deployment. Next, the inner layer adopts a greedy algorithm to optimize computation offloading. The extensive simulation experiments verify the feasibility and effectiveness of the proposed PSO-GA-G. The results show that the PSO-GA-G can achieve a lower average task response time than the other three baselines.

show abstract

Energy-Efficient Multi-UAV-Enabled Multiaccess Edge Computing Incorporating NOMA

Cited by 135 publications

References 49 publications

Cognitive UAV-Aided URLLC and mMTC Services: Analyzing Energy Efficiency and Latency

Cognitive UAV-Aided URLLC and mMTC Services: Analyzing Energy Efficiency and Latency

Secrecy Energy Efficiency Maximization for UAV Swarm Assisted Multi-Hop Relay System: Joint Trajectory Design and Power Control

Joint computation offloading and deployment optimization in multi-UAV-enabled MEC systems

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