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学者姓名：赵宜升
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Aiming at the problem of low data rate in autonomous underwater vehicle (AUV)-based underwater acoustic communication system, a hybrid magnetic induction (MI) and reconfigurable intelligent surface (RIS)-assisted communication strategy is proposed to maximize system channel capacity. Specifically, an AUV first collects data from all the seafloor nodes by adopting the MI communication technology. Then, the AUV forwards the data to another AUV carried with a RIS by using the underwater acoustic communication method. With the help of the RIS, a strong reflective path between the first AUV and a surface base station (BS) on the sea is formed. The surface BS could receive the data at a relatively high data rate. In order to maximize the system channel capacity, the acoustic incidence angle, the distance between the first AUV and the RIS, the distance between the RIS and the surface BS, acoustic signal frequency, and transmitting power are jointly optimized. The formulated optimization problem is solved by employing a butterfly optimization algorithm (BOA) and an improved butterfly optimization algorithm (IBOA), respectively. Simulation results show that the IBOA can increase the system channel capacity more effectively than the basic BOA.
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| GB/T 7714 | Hu, Zhiyi , Zhao, Yisheng , Liu, Peng et al. Hybrid MI and RIS-Assisted Acoustic Communication for Channel Capacity Maximization in AUV-Based UWAC System [J]. | 2024 IEEE 99TH VEHICULAR TECHNOLOGY CONFERENCE, VTC2024-SPRING , 2024 . |
| MLA | Hu, Zhiyi et al. "Hybrid MI and RIS-Assisted Acoustic Communication for Channel Capacity Maximization in AUV-Based UWAC System" . | 2024 IEEE 99TH VEHICULAR TECHNOLOGY CONFERENCE, VTC2024-SPRING (2024) . |
| APA | Hu, Zhiyi , Zhao, Yisheng , Liu, Peng , Song, Chaohua , Li, Tengteng . Hybrid MI and RIS-Assisted Acoustic Communication for Channel Capacity Maximization in AUV-Based UWAC System . | 2024 IEEE 99TH VEHICULAR TECHNOLOGY CONFERENCE, VTC2024-SPRING , 2024 . |
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Traditional underwater wireless communication in single medium has the limitations of low rate and high delay. In this paper, magnetic induction (MI)-based cross-medium communication is taken into account to reduce the transmission delay. Specifically, multiple autonomous underwater vehicles are used to collect data from underwater sensor nodes by MI communication. The collected data is directly transferred to a unmanned aerial vehicle above the water via ultra-low frequency MI communication. The cross-medium data collection and transmission problem is formulated an optimization problem. The objective is to minimize the total delay under the constraints of transmitting power, transmission distance, and number of turns of MI coil. A standard particle swarm optimization (SPSO) algorithm and a quantum-behaved particle swarm optimization (QPSO) algorithm are adopted to obtain the suboptimal solution, respectively. Simulation results show that the QPSO algorithm is superior to the SPSO algorithm in reducing the total delay.
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| GB/T 7714 | Liu, Peng , Zhao, Yisheng , Hu, Zhiyi et al. MI-Based Cross-Medium Communication for Multi-AUV-Assisted Underwater Data Acquisition [J]. | 2024 IEEE 99TH VEHICULAR TECHNOLOGY CONFERENCE, VTC2024-SPRING , 2024 . |
| MLA | Liu, Peng et al. "MI-Based Cross-Medium Communication for Multi-AUV-Assisted Underwater Data Acquisition" . | 2024 IEEE 99TH VEHICULAR TECHNOLOGY CONFERENCE, VTC2024-SPRING (2024) . |
| APA | Liu, Peng , Zhao, Yisheng , Hu, Zhiyi , Song, Chaohua , Li, Tengteng . MI-Based Cross-Medium Communication for Multi-AUV-Assisted Underwater Data Acquisition . | 2024 IEEE 99TH VEHICULAR TECHNOLOGY CONFERENCE, VTC2024-SPRING , 2024 . |
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The traditional underwater wireless sensor network (UWSN) based on acoustic communication has the shortcomings of low data rate and limited battery power. In this paper, hybrid acoustic and magnetic induction (MI) communication are considered to overcome the above drawbacks. A resource allocation strategy in autonomous underwater vehicle (AUV)-assisted edge computing UWSN is investigated to minimize the total system delay. Specifically, all the sensor nodes (SNs) are divided into different clusters. The SNs within a cluster send the data to the cluster head (CH) via the acoustic communication. The CH forwards the data to the AUV by the MI communication. Then, the AUV moves to the position under a surface vehicle (SV) carried with a edge server. The AUV forwards the data to the edge server through the MI communication. The transmitting power, channel bandwidth, and computational resources are jointly optimized. The formulated non-convex optimization problem is solved by using an alternating iterative optimization algorithm. Compared with other schemes, the proposed strategy can reduce the total system delay more effectively.
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| GB/T 7714 | Li, Tengteng , Zhao, Yisheng , Hu, Zhiyi et al. Resource Allocation Strategy in AUV-Assisted Edge Computing UWSN with Hybrid Acoustic and MI Communication [J]. | 2024 IEEE 99TH VEHICULAR TECHNOLOGY CONFERENCE, VTC2024-SPRING , 2024 . |
| MLA | Li, Tengteng et al. "Resource Allocation Strategy in AUV-Assisted Edge Computing UWSN with Hybrid Acoustic and MI Communication" . | 2024 IEEE 99TH VEHICULAR TECHNOLOGY CONFERENCE, VTC2024-SPRING (2024) . |
| APA | Li, Tengteng , Zhao, Yisheng , Hu, Zhiyi , Song, Chaohua , Liu, Peng . Resource Allocation Strategy in AUV-Assisted Edge Computing UWSN with Hybrid Acoustic and MI Communication . | 2024 IEEE 99TH VEHICULAR TECHNOLOGY CONFERENCE, VTC2024-SPRING , 2024 . |
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Aiming at the problem of limited energy stored in unmanned aerial vehicle (UAV), a resource allocation strategy for UAV-assisted edge computing in wireless powered communication networks is investigated in this paper. By deploying a laser beam director on the ground, sufficient energy can be provided for the UAV in a short period of time. Then, multiple ground terminals obtain energy from this UAV by radio frequency energy harvesting method and offload their computational tasks to the UAV with edge server. The resource allocation problem is modeled as an optimization problem. The optimization objective is to minimize the total energy consumption of the UAV subject to the constraints of energy and data causality, computational resources, and transmitting power. The suboptimal solution is obtained by introducing an imperialist competitive algorithm. Simulation results show that the imperialist competitive algorithm consumes less energy compared with the particle swarm optimization algorithm and the equal upload time allocation method.
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| GB/T 7714 | Zhang, Xinyu , Zhao, Yisheng , You, Hongyi et al. Resource Allocation Strategy for Wireless Powered Communication Networks with UAV-Assisted Edge Computing [J]. | 2024 IEEE 99TH VEHICULAR TECHNOLOGY CONFERENCE, VTC2024-SPRING , 2024 . |
| MLA | Zhang, Xinyu et al. "Resource Allocation Strategy for Wireless Powered Communication Networks with UAV-Assisted Edge Computing" . | 2024 IEEE 99TH VEHICULAR TECHNOLOGY CONFERENCE, VTC2024-SPRING (2024) . |
| APA | Zhang, Xinyu , Zhao, Yisheng , You, Hongyi , Jian, Kaige , Liang, Li . Resource Allocation Strategy for Wireless Powered Communication Networks with UAV-Assisted Edge Computing . | 2024 IEEE 99TH VEHICULAR TECHNOLOGY CONFERENCE, VTC2024-SPRING , 2024 . |
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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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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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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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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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