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Age of Information Based user scheduling and Data Assignment in Multi-user Mobile Edge Computing Networks:An Online Algorithm

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China Communications 2024年第5期21卷 153-165页

作者： Ge Yiyang Xiong Ke Dong Rui Lu Yang Fan Pingyi Qu Gang School of Computer and Information Technology Beijing Jiaotong UniversityBeijing 100044China Engineering Research Center of Network Management Technology for High Speed Railway of Ministry of Education Beijing 100044China Beijing Engineering Research Center of High-speed Railway Broadband Mobile Communications Beijing 100044China Department of Electronic Engineering Tsinghua UniversityBeijing 100084China Beijing National Research Center for Information Science and Technology Tsinghua UniversityBeijing 100084China Department of Electrical and Computer Engineering College ParkMarylandUSA

This paper investigates the age of information(AoI)-based multi-user mobile edge computing(MEC)network with partial offloading *** weighted sum AoI(WSA)is first analyzed and derived,and then a WSA minimization problem is formulated by jointly optimizing the user scheduling and data *** to the non-analytic expression of the WSA *** optimization variables and the unknowability of future network information,the problem cannot be solved with known solution ***,an online Joint Partial Offloading and user scheduling Optimization(JPOUSO)algorithm is proposed by transforming the original problem into a single-slot data assignment subproblem and a single-slot user scheduling sub-problem and solving the two sub-problems *** analyze the computational complexity of the presented JPO-USO algorithm,which is of O(N),with N being the number of *** results show that the proposed JPO-USO algorithm is able to achieve better AoI performance compared with various baseline *** is shown that both the user’s data assignment and the user’s AoI should be jointly taken into account to decrease the system WSA when scheduling users.

关键词： age of information(aoi) mobile edge computing(mec) user scheduling

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Multitask deep learning-based multiuser hybrid beamforming for mm-wave orthogonal frequency division multiple access systems

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Science China(Information Sciences) 2020年第8期63卷 39-49页

作者： Jing JIANG Yue LI Long CHEN Jianbo DU Chunguo LI School of Information and Communication Engineering Xi'an University of Posts and Telecommunications School of Information Science and Engineering Southeast University

Multiuser hybrid beamforming of a wideband millimeter-wave(mm-wave) system is a complex combinatorial optimization problem. It not only needs large training data, but also tends to overfit and incur long run-time when multiple serial deep learning network models are used to solve this problem ***, multitask deep learning(MTDL) model could jointly learn multiple related tasks and share their knowledge among the tasks, and this has been demonstrated to improve performance, compared to learning the tasks individually. Therefore, this work presents a first attempt to exploit MTDL for multiuser hybrid beamforming for mm-wave massive multiple-input multiple-output orthogonal frequency division multiple access systems. The MTDL model includes a multitask network architecture, which consists of two tasksuser scheduling and multiuser analog beamforming. First, we use the effective channel with a low dimension as input data for the two parallel tasks to reduce the computational complexity of deep neural *** a shallow shared layer of the MTDL model, we utilize hard parameter sharing in which the knowledge of multiuser analog beamforming task is shared with the user scheduling task to mitigate multiuser interference. Second, in the training process of the MTDL model, we use the exhaustive search algorithm to generate training data to ensure optimal performance. Finally, we choose the weight coefficient of each task by traversing all weight coefficient combinations in the training phase. Simulation results prove that our proposed MTDL-based multiuser hybrid beamforming scheme could achieve better performance than traditional algorithms and multiple serial single tasks deep learning scheme.

关键词： millimeter wave systems user scheduling deep neural network multitask learning hybrid beamforming

Clustering and scheduling methods based on SLNR in downlink CoMP system

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The Journal of China Universities of Posts and Telecommunications 2014年第1期21卷 74-78,103页

作者： WU Xu-guang LI Yi JI Hong School of Telecommunication Engineering Beijing University of Posts and Telecommunications

In downlink coordinated multi-point（CoMP） system, full cooperation is always not applicable in real world because of its high request in the backhaul. To deal with this problem, clustering decision is made to process transmission. In this paper clustering methods based on the metric signal-to-leakage-plus-noise（SLNR） is proposed. In addition, user scheduling schemes based on SLNR is also put up to make the scheduling set as large as possible. Simulation results show that the proposed clustering methods not only reduce the data sharing among the cooperating base stations（BSs）, but also improve the system throughput compared with the traditional clustering methods based on channel strength.

关键词： CoMP clustering formation SLNR user scheduling

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Secure NOMA and OMA coordinated transmission schemes in untrusted relay networks

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Science China(Information Sciences) 2021年第10期64卷 266-267页

作者： Lu LV Zan LI Haiyang DING Jian CHEN State Key Laboratory of Integrated Services Networks Xidian University National Mobile Communications Research Laboratory Southeast University School of Information and Communications National University of Defense Technology

Dear editor,Safeguarding physical-layer non-orthogonal multiple access(NOMA) transmissions using relays has been received widespread attention [1, 2]. However, a relay may be datalevel untrusted and serve as an eavesdropper to decode its forwarded messages. Hence, information security needs to be guaranteed if an untrusted relay is involved.

关键词： physical-layer security non-orthogonal multiple access cooperative jamming user scheduling untrusted relay