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学者姓名：赵宜升
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Due to the limited coverage of base station (BS) and battery capacity of mobile users, the resource allocation strategy in multiple unmanned aerial vehicles (UAVs)-assisted edge computing system with nonlinear energy harvesting is investigated in this paper. The cooperation between BS and multi-UAV is considered, which can provide extensive coverage for users with mobility. Mobile users can simultaneously offload computation bits to the BS and the UAV, and mobile users harvest energy from BS and UAV. Meanwhile, the mobility of users is taken into account. Moreover, an echo state network (ESN)-based prediction algorithm is utilized for predicting the future positions of mobile users. Therefore, the UAV can reach the predicted users' positions in advance to ensure the continuity of communication. The objective of the resource allocation strategy is to maximize the energy efficiency by jointly optimizing bandwidth allocation, computation resources, the trajectory of UAV, and transmitting power of mobile users. Then, the resource allocation problem is formulated as a mixed-integer nonlinear programming problem. The quantum-behaved particle swarm optimization (QPSO) algorithm is used to solve the problem. Simulation results demonstrate that the proposed strategy can achieve higher energy efficiency than other benchmark strategies. In addition, QPSO algorithm outperforms the standard particle swarm optimization algorithm and genetic algorithm in terms of energy efficiency.
Keyword ：
energy harvesting energy harvesting mobile communication mobile communication mobile computing mobile computing resource allocation resource allocation
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| GB/T 7714 | Chen, Yong , Zhao, Yisheng , He, Ximei et al. Resource allocation method for Mobility-Aware and Multi-UAV-Assisted mobile edge computing systems with energy harvesting [J]. | IET COMMUNICATIONS , 2023 , 17 (8) : 960-973 . |
| MLA | Chen, Yong et al. "Resource allocation method for Mobility-Aware and Multi-UAV-Assisted mobile edge computing systems with energy harvesting" . | IET COMMUNICATIONS 17 . 8 (2023) : 960-973 . |
| APA | Chen, Yong , Zhao, Yisheng , He, Ximei , Xu, Zhihong . Resource allocation method for Mobility-Aware and Multi-UAV-Assisted mobile edge computing systems with energy harvesting . | IET COMMUNICATIONS , 2023 , 17 (8) , 960-973 . |
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Aimed at the doubly near-far problems in a large range suffered by the remote user group and in a small range existing in both nearby and remote user groups during energy harvesting and computation offloading, a resource allocation method for unmanned aerial vehicle (UAV)-assisted and user cooperation non-linear energy harvesting mobile edge computing (MEC) system is proposed. The UAV equipped with an MEC server is introduced to provide energy and computing services for the remote user group to alleviate the doubly near-far problem in a large range suffered by the remote user group. The doubly near-far problem in a small range existing in both nearby and remote user groups is mitigated by user cooperation. The specific user cooperation strategy is that the user near the base station or the UAV is used as a relay to transfer the computing task of the user far from the base station or the UAV to the MEC server for computing. By jointly optimizing users’ offloading time, users’ transmitting power, and the hovering position of the UAV, the resource allocation problem is modeled as a nonlinear programming problem with the objective of maximizing computation efficiency. The suboptimal solution is obtained by adopting the differential evolution algorithm. Simulation results show that, compared with the resource allocation method based on genetic algorithm and the without user cooperation method, the proposed method has higher computation efficiency. © 2023, Shanghai Jiao Tong University.
Keyword ：
A A mobile edge computing (MEC) mobile edge computing (MEC) non-linear energy harvesting non-linear energy harvesting resource allocation resource allocation TN 915.65 TN 915.65 unmanned aerial vehicle (UAV) unmanned aerial vehicle (UAV) user cooperation user cooperation
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| GB/T 7714 | He, X. , Zhao, Y. , Xu, Z. et al. Resource Allocation Method for Unmanned Aerial Vehicle-Assisted and User Cooperation Non-Linear Energy Harvesting Mobile Edge Computing System; [无人机协助和用户协作的非线性能量收集移动边缘计算系统资源分配方法] [J]. | Journal of Shanghai Jiaotong University (Science) , 2023 . |
| MLA | He, X. et al. "Resource Allocation Method for Unmanned Aerial Vehicle-Assisted and User Cooperation Non-Linear Energy Harvesting Mobile Edge Computing System; [无人机协助和用户协作的非线性能量收集移动边缘计算系统资源分配方法]" . | Journal of Shanghai Jiaotong University (Science) (2023) . |
| APA | He, X. , Zhao, Y. , Xu, Z. , Chen, Y. . Resource Allocation Method for Unmanned Aerial Vehicle-Assisted and User Cooperation Non-Linear Energy Harvesting Mobile Edge Computing System; [无人机协助和用户协作的非线性能量收集移动边缘计算系统资源分配方法] . | Journal of Shanghai Jiaotong University (Science) , 2023 . |
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Mobile edge computing (MEC) moves computeintensive tasks to the edge of wireless networks, which can effectively reduce service latency and improve quality of service. A resource allocation strategy for multiple unmanned aerial vehicles-supported MEC system with dense mobile users (MU) is investigated in this paper. By applying a magnetically coupled resonance wireless power transfer technology, the MU can harvest enough energy from a wireless charging station in a short time. The models of MU energy harvesting, data transmission, and task computation are analyzed. Under the constraints of energy causality, CPU computing resources, channel bandwidth, and transmitting power, the resource allocation problem for minimizing system latency is established. A quantum-behaved particle swarm optimization (QPSO) algorithm and a standard particle swarm optimization (SPSO) algorithm are used to obtain the suboptimal solution. Simulation results show that the QPSO algorithm is more effective in reducing system latency compared to the SPSO algorithm and the benchmark scheme.
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| GB/T 7714 | Liang, Li , Zhao, Yisheng , Jian, Kaige et al. Resource Allocation Strategy for Multi-UAV-Assisted MEC System with Dense Mobile Users and MCR-WPT [J]. | 2023 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE, WCNC , 2023 . |
| MLA | Liang, Li et al. "Resource Allocation Strategy for Multi-UAV-Assisted MEC System with Dense Mobile Users and MCR-WPT" . | 2023 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE, WCNC (2023) . |
| APA | Liang, Li , Zhao, Yisheng , Jian, Kaige , You, Hongyi , Zhang, Xinyu . Resource Allocation Strategy for Multi-UAV-Assisted MEC System with Dense Mobile Users and MCR-WPT . | 2023 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE, WCNC , 2023 . |
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针对在能量收集和计算任务卸载过程中距离基站较远的用户设备遭受的双重远近问题,提出了一种无人机(Unmanned Aerial Vehicle,UAV)协助的非线性能量收集移动边缘计算(Mobile Edge Computing,MEC)系统资源分配方法.近距离用户设备由搭载MEC服务器的基站为其补充能量和提供计算服务,通过引入搭载MEC服务器的UAV为远距离用户设备补充能量并提供计算服务以缓解其遭受的双重远近问题.在满足用户设备和UAV的能量消耗以及UAV速度等约束条件下,以最大化系统计算完成的数据量为目标,将资源分配问题建模成非线性规划问题,利用差分进化算法,得到次优解.仿真结果表明,与基于遗传算法的资源分配方法和基于差分进化算法的固定功率分配方法相比,所提方法的系统计算完成数据量分别提升了 25.8％和10.0％,能够有效地缓解双重远近问题.
Keyword ：
双重远近问题 双重远近问题 差分进化算法 差分进化算法 无人机 无人机 移动边缘计算 移动边缘计算 资源分配 资源分配 非线性能量收集 非线性能量收集
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| GB/T 7714 | 贺喜梅 , 赵宜升 , 徐志红 et al. UAV协助的能量收集MEC系统资源分配方法 [J]. | 西安邮电大学学报 , 2023 , 27 (3) : 21-29 . |
| MLA | 贺喜梅 et al. "UAV协助的能量收集MEC系统资源分配方法" . | 西安邮电大学学报 27 . 3 (2023) : 21-29 . |
| APA | 贺喜梅 , 赵宜升 , 徐志红 , 陈勇 . UAV协助的能量收集MEC系统资源分配方法 . | 西安邮电大学学报 , 2023 , 27 (3) , 21-29 . |
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Aiming at excessive users existing in a pico base station (PBS) in the multi-layer heterogeneous networks, the resource allocation problem of maximizing the energy efficiency of the networks is investigated in this paper. By deploying a relay node with energy harvesting function, the data of some users in the PBS can be transferred to an adjacent idle PBS. The bandwidth and transmitting power of users and the relay node are both considered to formulate the resource allocation optimization problem. The objective is to maximize the energy efficiency of the whole heterogeneous networks under the constraints of the user’s minimum data rate and energy consumption. The suboptimal solution is obtained by using the particle swarm optimization (PSO) algorithm and quantum-behaved particle swarm optimization (QPSO) algorithm. Simulation results show that the adopted methods have higher energy efficiency than the conventional fixed power and bandwidth method. In addition, the time complexity of the adopted methods is relatively low. © 2021, Shanghai Jiao Tong University and Springer-Verlag GmbH Germany, part of Springer Nature.
Keyword ：
A A energy efficiency energy efficiency energy harvesting energy harvesting heterogeneous networks heterogeneous networks TN 915.65 TN 915.65
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| GB/T 7714 | Gao, J. , Zhao, Y. , Chen, J. et al. Energy-Efficient Bandwidth and Power Allocation in Relay-Assisted Multi-Layer Heterogeneous Networks with Energy Harvesting; [具有能量收集的中继辅助多层异构网络的能量高效带宽和功率分配策略] [J]. | Journal of Shanghai Jiaotong University (Science) , 2023 , 28 (6) : 822-830 . |
| MLA | Gao, J. et al. "Energy-Efficient Bandwidth and Power Allocation in Relay-Assisted Multi-Layer Heterogeneous Networks with Energy Harvesting; [具有能量收集的中继辅助多层异构网络的能量高效带宽和功率分配策略]" . | Journal of Shanghai Jiaotong University (Science) 28 . 6 (2023) : 822-830 . |
| APA | Gao, J. , Zhao, Y. , Chen, J. , Chen, Z. . Energy-Efficient Bandwidth and Power Allocation in Relay-Assisted Multi-Layer Heterogeneous Networks with Energy Harvesting; [具有能量收集的中继辅助多层异构网络的能量高效带宽和功率分配策略] . | Journal of Shanghai Jiaotong University (Science) , 2023 , 28 (6) , 822-830 . |
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本发明提出一种基于MCR‑WPT的无人机协助WPCN的资源分配方法，在原有无人机协助的WPCN中，再引入一个搭载MCR装置的能量传输无人机。同时，每个地面终端安装接收线圈。通过MCR方式，能量传输无人机依次为地面终端提供足够的能量。为了使所有地面终端的最小吞吐量最大化，联合优化信息接收无人机轨迹、地面终端发射功率和时隙分配比例，将该问题建模成最优化问题。由于该问题是非凸优化问题，不适合直接求解。通过引入一些辅助变量，将一些非凸约束条件通过适当的数学推导转换成凸约束条件。对于难以转化的非凸约束条件，采用凹凸过程将非凸函数线性化成两个凸函数相减的形式。最后通过迭代求解原非凸逼近问题的凸逼近问题，得到原始问题的次优解。
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| GB/T 7714 | 赵宜升 , 徐志红 , 贺喜梅 et al. 基于MCR-WPT的无人机协助WPCN的资源分配方法 : CN202210483396.4[P]. | 2022-05-05 00:00:00 . |
| MLA | 赵宜升 et al. "基于MCR-WPT的无人机协助WPCN的资源分配方法" : CN202210483396.4. | 2022-05-05 00:00:00 . |
| APA | 赵宜升 , 徐志红 , 贺喜梅 , 陈勇 . 基于MCR-WPT的无人机协助WPCN的资源分配方法 : CN202210483396.4. | 2022-05-05 00:00:00 . |
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Aiming at the problem that the ground terminals (GTs) often suffer from difficulties in energy harvesting and data processing in remote areas, a resource allocation strategy for layered unmanned aerial vehicles (UAVs)-assisted mobile edge computing system is investigated in this paper. According to different functions, the UAVs are deployed in three layers. By using a magnetic coupling resonance wireless power transfer (MCR-WPT) technology, the GTs can obtain sufficient energy from the first layer of UAVs equipped with transmitting coils. The computational tasks of the GTs are divided into popular, private, and non-popular tasks. The popular and private tasks are both offloaded to the second layer of popular tasks UAVs (PT-UAVs), while the non-popular tasks are offloaded to the third layer of non-popular tasks UAV (NPT-UAV). The resource allocation problem is formulated as an optimization problem. The optimization objective is to minimize the system overhead by jointly optimizing the PT-UAVs caching policy, the GTs partial offloading factor, the charging time of the GTs, the trajectory of the NPT-UAV, and the bandwidth and computational resource of the system. The suboptimal solution is derived by introducing a social learning particle swarm optimization (SLPSO) algorithm. Simulation results show that the SLPSO algorithm outperforms other benchmark methods in terms of the system overhead. © 2023 IEEE.
Keyword ：
Antennas Antennas Computation offloading Computation offloading Data handling Data handling Energy harvesting Energy harvesting Energy transfer Energy transfer Inductive power transmission Inductive power transmission Internet of things Internet of things Mobile edge computing Mobile edge computing Particle swarm optimization (PSO) Particle swarm optimization (PSO) Resource allocation Resource allocation Unmanned aerial vehicles (UAV) Unmanned aerial vehicles (UAV)
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| GB/T 7714 | Zhang, Xinyu , Zhao, Yisheng , You, Hongyi et al. SLPSO-Based Resource Allocation Strategy for Layered UAVs-Assisted MEC System with MCR-WPT [C] . 2023 : 615-621 . |
| MLA | Zhang, Xinyu et al. "SLPSO-Based Resource Allocation Strategy for Layered UAVs-Assisted MEC System with MCR-WPT" . (2023) : 615-621 . |
| APA | Zhang, Xinyu , Zhao, Yisheng , You, Hongyi , Liang, Li , Jian, Kaige . SLPSO-Based Resource Allocation Strategy for Layered UAVs-Assisted MEC System with MCR-WPT . (2023) : 615-621 . |
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Aiming at the threat of eavesdropping in unmanned aerial vehicle (UAV)-assisted communication networks, multiuser secure communication with multi-relay nodes and multi-eavesdropping threats is investigated in this paper. By deploying a dual-antenna UAV relay node, a hybrid decode-and-forward (DF) and amplify-and-forward (AF) protocol is considered to transmit data. In addition, cooperative jamming techniques are used in order to improve the performance of the secure confidential communication. The secure communication problem is formulated as an optimization problem. The goal is to maximize the minimum secrecy transmission rate subject to UAV transmitting power, user transmitting power, and channel bandwidth. By introducing a grey wolf optimizer (GWO), the suboptimal solution is obtained. Simulation results show that the GWO has higher secrecy transmission rate than the firefly algorithm and the average bandwidth and fixed transmitting power method. © 2023 IEEE.
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| GB/T 7714 | Jian, K. , Zhao, Y. , Liang, L. et al. UAV-Assisted Multi-User Secure Communication Based on Hybrid DF and AF Protocol [未知]. |
| MLA | Jian, K. et al. "UAV-Assisted Multi-User Secure Communication Based on Hybrid DF and AF Protocol" [未知]. |
| APA | Jian, K. , Zhao, Y. , Liang, L. , You, H. , Zhang, X. . UAV-Assisted Multi-User Secure Communication Based on Hybrid DF and AF Protocol [未知]. |
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In the mobile edge computing (MEC) scenarios, users outside the coverage area of the base station are unable to offload tasks during the offloading process. In this paper, a resource allocation strategy in unmanned aerial vehicle (UAV)-assisted device-to-device (D2D) multi-relay MEC system with energy harvesting is proposed. Multiple UAVs are deployed around the edge of the base station (BS) coverage area. D2D link between users outside the BS coverage area and edge users of BS is controlled by the UAV. Edge users of BS can harvest energy from the UAV. Computing tasks of users outside the BS coverage area can be sent to edge users of BS by different D2D links. Then, edge users of BS can forward computing tasks to the MEC server of BS. Transmitting power, energy harvesting time, computing resources, and channel bandwidth are jointly optimized to minimize the total task completion time. The formulated problem is a non-convex optimization problem. By introducing a series of auxiliary variables, a perspective function is used to convert some non-convex constraints into convex constraints. An alternate iterative optimization algorithm is adopted to obtain the optimal solution to the original problem. Simulation results show that compared with other baseline schemes, the proposed strategy can reduce the total task completion time more effectively. © 2023 IEEE.
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| GB/T 7714 | You, H. , Zhao, Y. , Jian, K. et al. Resource Allocation Strategy in UAV-D2D-Aided Multi-Relay MEC System with Energy Harvesting [未知]. |
| MLA | You, H. et al. "Resource Allocation Strategy in UAV-D2D-Aided Multi-Relay MEC System with Energy Harvesting" [未知]. |
| APA | You, H. , Zhao, Y. , Jian, K. , Liang, L. , Zhang, X. . Resource Allocation Strategy in UAV-D2D-Aided Multi-Relay MEC System with Energy Harvesting [未知]. |
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Aiming at the the characteristic of dependency relationship existing among multiple tasks, a resource allocation strategy for multi-task mobile edge computing systems is investigated in this paper. Sequential dependency relationship among multiple tasks is taken into account. When the current task completes offloading, the next task can be offloaded without waiting for the current task to finish computing. By using a two-tier offloading strategy, when the edge server in small base station has insufficient computing capacity, the offloading task could be further divided and offloaded to the edge server in macro base station with sufficient computation resources. The resource allocation problem is formulated as an optimization problem. The objective is to minimize the total computation time of the overall system under the constraints of computing capability range of user, maximal computing resource of edge server, and maximal transmitting power of user. To solve the formulated optimization problem, a suboptimal solution is obtained by adopting a quantum-behaved particle swarm optimization (QPSO) algorithm. Simulation results show that the performance of the proposed strategy is superior to other benchmark strategies, and QPSO algorithm has less computation time compared with the standard particle swarm optimization algorithm.
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| GB/T 7714 | Chen, Yong , Zhao, Yisheng , He, Ximei et al. Resource Allocation Method for Minimizing Total Computation Time in Multi-Task Mobile Edge Computing Systems [J]. | 2022 IEEE 95TH VEHICULAR TECHNOLOGY CONFERENCE (VTC2022-SPRING) , 2022 . |
| MLA | Chen, Yong et al. "Resource Allocation Method for Minimizing Total Computation Time in Multi-Task Mobile Edge Computing Systems" . | 2022 IEEE 95TH VEHICULAR TECHNOLOGY CONFERENCE (VTC2022-SPRING) (2022) . |
| APA | Chen, Yong , Zhao, Yisheng , He, Ximei , Xu, Zhihong . Resource Allocation Method for Minimizing Total Computation Time in Multi-Task Mobile Edge Computing Systems . | 2022 IEEE 95TH VEHICULAR TECHNOLOGY CONFERENCE (VTC2022-SPRING) , 2022 . |
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