Jianbo Du

Xi’an University of Posts and Telecommunications

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F. Richard Yu

Carleton University

Xiaoli Chu

University of Sheffield

Jie Feng

Xidian University

Haotong Cao

Nanjing University of Posts and Telecommunications

Daojing Li

Chinese Academy of Sciences

Aijing Sun

Xi’an University of Posts and Telecommunications

Liqiang Zhao

Xidian University

Guangyue Lu

Xi’an University of Posts and Telecommunications

Jing Jiang

Xi’an University of Posts and Telecommunications

Zhiwei Zuo

Shanghai Institute of Organic Chemistry

Cooperative Institutions

Defence Electronics Research Laboratory

Xi’an University of Posts and Telecommunications

Chinese Academy of Sciences

Xidian University

University of Chinese Academy of Sciences

China Academy of Space Technology

Northwestern Polytechnical University

Beihang University

Huazhong University of Science and Technology

Zhejiang University

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Computation Offloading and Resource Allocation in UAV-Assisted Satellite Network Systems

2022 IEEE/CIC International Conference on Communications in China (ICCC) (2024)

Bintao Hu Yuan Gao Miguel López‐Benítez Jianbo Du Jie Zhang

Computation offloading

Resource Management

10.1109/iccc62479.2024.10681822

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CCDC 2208820: Experimental Crystal Structure Determination

The Cambridge Structural Database (2022)

Qing Long An Yang‐Yang Xing Ruihua Pu Menghui Jia Yue-Gang Chen

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.

10.5517/ccdc.csd.cc2d4g89

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ミリ波ベースのミリ波ミリ波の移動目標の三次元画像化について述べた。【JST・京大機械翻訳】

Hongwai yu Haomibo Xuebao (2016)

Ma Meng Daojing Li Liechen Li Jianbo Du Qiao Ming

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Distributionally robust optimization based chance-constrained energy management for hybrid energy powered cellular networks

Digital Communications and Networks (2022)

Pengfei Du Hongjiang Lei Imran Shafique Ansari Jianbo Du Xiaoli Chu

Energy harvesting has been recognized as a promising technique with which to effectively reduce carbon emissions and electricity expenses of base stations. However, renewable energy is inherently stochastic and intermittent, imposing formidable challenges on reliably satisfying users' time-varying wireless traffic demands. In addition, the probability distribution of the renewable energy or users' wireless traffic demand is not always fully known in practice. In this paper, we minimize the total energy cost of a hybrid-energy-powered cellular network by jointly optimizing the energy sharing among base stations, the battery charging and discharging rates, and the energy purchased from the grid under the constraint of a limited battery size at each base station. In solving the formulated non-convex chance-constrained stochastic optimization problem, a new ambiguity set is built to characterize the uncertainties in the renewable energy and wireless traffic demands according to interval sets of the mean and covariance. Using this ambiguity set, the original optimization problem is transformed into a more tractable second-order cone programming problem by exploiting the distributionally robust optimization approach. Furthermore, a low-complexity distributionally robust chance-constrained energy management algorithm, which requires only interval sets of the mean and covariance of stochastic parameters, is proposed. The results of extensive simulation are presented to demonstrate that the proposed algorithm outperforms existing methods in terms of the computational complexity, energy cost, and reliability.

Robust Optimization

10.1016/j.dcan.2022.06.001

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Task offloading, load balancing, and resource allocation in MEC networks

IET Communications (2020)

Silei Li Jianbo Du Daosen Zhai Xiaoli Chu F. Richard Yu

To prolong the time duration of smart mobile devices (SMDs) or enable low‐latency tasks, mobile edge computing (MEC) has emerged as a promising paradigm by offloading tasks to nearby MEC servers (MECSs). In this study the authors propose an optimisation problem to minimise the weighted sum of the total delay and energy consumption of all SMDs in a multi‐MECS‐multi‐SMD network via multi‐dimensional optimisation on offloading strategy making, load balancing, computation resource allocation and transmit power control. Since the problem is NP‐hard, the authors decompose it into three subproblems to solve. First, they propose a low complexity heuristic algorithm to obtain the offloading strategies while guaranteeing load balancing between the multiple MECSs. Then they solve computation resource allocation subproblem using Lagrange dual decomposition. Finally, employing fractional programming, the authors transform the transmit power control subproblem into a convex programming problem where the closed‐form solution is obtained. The proposed simulation results verify the convergence of the proposed iterative algorithms, and demonstrate that the proposed joint optimisation could achieve good performance in both delay and energy reduction.

Resource Management

10.1049/iet-com.2018.6122

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Citations (21)

Sparse flight spotlight mode 3-D imaging of spaceborne SAR based on sparse spectrum and principal component analysis

Journal of Systems Engineering and Electronics (2021)

Kai Zhou Daojing Li Anjing Cui Han Dong Tian Xiao He

The spaceborne synthetic aperture radar (SAR) sparse flight 3-D imaging technology through multiple observations of the cross-track direction is designed to form the crosstrack equivalent aperture, and achieve the third dimensionality recognition.In this paper, combined with the actual triple star orbits, a sparse flight spaceborne SAR 3-D imaging method based on the sparse spectrum of interferometry and the principal component analysis (PCA) is presented.Firstly, interferometric processing is utilized to reach an effective sparse representation of radar images in the frequency domain.Secondly, as a method with simple principle and fast calculation, the PCA is introduced to extract the main features of the image spectrum according to its principal characteristics.Finally, the 3-D image can be obtained by inverse transformation of the reconstructed spectrum by the PCA.The simulation results of 4.84 km equivalent crosstrack aperture and corresponding 1.78 m cross-track resolution verify the effective suppression of this method on high-frequency sidelobe noise introduced by sparse flight with a sparsity of 49% and random noise introduced by the receiver.Meanwhile, due to the influence of orbit distribution of the actual triple star orbits, the simulation results of the sparse flight with the 7-bit Barker code orbits are given as a comparison and reference to illuminate the significance of orbit distribution for this reconstruction results.This method has prospects for sparse flight 3-D imaging in high latitude areas for its short revisit period.

10.23919/jsee.2021.000098

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Task placement and resource allocation for UAV and edge computing supported transportation systems

The Journal of Supercomputing (2024)

Jianbo Du Jianjun Zhang Jianxin Li Jiaju Lv Aijing Sun

10.1007/s11227-024-06647-z

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Cost-Effective Task Offloading in NOMA-Enabled Vehicular Mobile Edge Computing

IEEE Systems Journal (2022)

Jianbo Du Yan Sun Ning Zhang Zehui Xiong Aijing Sun

Nonorthogonal multiple access (NOMA) and mobile edge computing (MEC) are two key emerging technologies for vehicular networks, where NOMA allows multiple vehicular user equipments (VUEs) to share the same wireless resources, and thus to enhance the spectrum utilization and system capacity, and MEC permits VUEs to offload their complex applications to MEC servers, and thus to provide support for computationally intensive intelligent applications. In this article, a NOMA-based vehicle edge computing (VEC) network model is proposed, and the cost minimization problem is constructed. Under the premise of ensuring the delay tolerance of all VUEs, the total system cost is minimized through the joint optimization of offloading decision-making, VUE clustering, subchannel and computation resource allocation, and transmission power control. Since the proposed problem is a mixed-integer nonlinear programming problem, which is difficult to solve, we decouple it into two subproblems and propose two heuristic algorithms to solve the task offloading and the MEC resource assignment problem, respectively, and finally, we obtain the closed-form solutions for cloud-related optimization problems through simple analysis. Simulation results show that the proposed joint algorithm is superior to other baseline algorithms in terms of system cost minimization.

Mobile Edge Computing

Lyapunov optimization

Resource Management

10.1109/jsyst.2022.3167901

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Citations (29)

Resource Pricing and Allocation in MEC Enabled Blockchain Systems: An A3C Deep Reinforcement Learning Approach

IEEE Transactions on Network Science and Engineering (2021)

Jianbo Du Wenjie Cheng Guangyue Lu Haotong Cao Xiaoli Chu

When using blockchain in mobile systems, computation intensive mining tasks pose great challenges to the processing capabilities of mobile miner equipment. Mobile edge computing (MEC) is an effective solution to alleviating the problem via task offloading. In the mining process, miners compete for rewards through puzzle solving, where only the miner that first completes the process will be rewarded. Thus, miners may wish to pay higher price and use more communication resources in task offloading and more computation resources in task processing for latency reduction. However, there are risks for the miners not profiting from consuming more resources or paying a higher price, so miners are rational in blockchain systems. In order to maximize the rational total profit of all miners, we use an asynchronous advantage actor-critic (A3C) deep reinforcement learning algorithm to obtain the resource pricing and allocation, considering the stochastic properties of wireless channels, and the prospect theory is employed to strike a good balance between risks and rewards. Numerical results show that our proposed A3C based joint optimization algorithm converges fast and outperforms the baseline algorithms in terms of the total reward.

Mobile Edge Computing

Profit maximization

10.1109/tnse.2021.3068340

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Citations (136)

AoI Optimization for UAV-Assisted Wireless Sensor Networks

2022 IEEE International Conference on Communications Workshops (ICC Workshops) (2024)

Aijing Sun Chi Sun Jianbo Du Chen Chen Chengbin Huang

10.1109/iccworkshops59551.2024.10615615

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