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computation efficiency optimization in UAV-enabled mobile edge computing system with multi-carrier non-orthogonal multiple access

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EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING 2020年第1期2020卷 1-22页

作者： Xu, Fangcheng Yu, Xiangbin Cai, Jiali Wang, Guangying Nanjing Univ Aeronaut & Astronaut Coll Elect & Informat Engn 29 Jiangjun Ave Nanjing 210016 Peoples R China Chinese Acad Sci Shanghai Inst Microsyst & Informat Technol Key Lab Wireless Sensor Network & Commun 865 Changning Rd Shanghai 200050 Peoples R China

In this paper, we study the issue of fair resource optimization for an unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) system with multi-carrier non-orthogonal multiple access (MC-NOMA). A computation efficiency (CE) optimization problem based on the max-min fairness principle under the partial offloading mode is formulated by optimizing the subchannel assignment, the local CPU frequency, and the transmission power jointly. The formulated problem belongs to the non-convex mixed integer nonlinear programming (MINLP), that is NP-hard to find the global optimal solution. Therefore, we design a polynomial-time algorithm based on the big-M reformulation, the penalized sequential convex programming, and the general Dinkelbach's method, which can choose an arbitrary point as the initial point and eventually converge to a feasible suboptimal solution. The proposed algorithm framework can be also applied to computation offloading only mode. Additionally, we derive the closed-form optimal solution under the local computing only mode. Simulation results validate the convergence performance of the proposed algorithm. Moreover, the proposed partial offloading mode with the CE maximization scheme outperforms that with the computation bits (CB) maximization scheme with respect to CE, and it can achieve higher CE than the benchmark computing modes. Furthermore, the proposed MC-NOMA scheme can attain better CE performance than the conventional OFDMA scheme.

关键词： Unmanned aerial vehicle Mobile edge computing Non-orthogonal multiple access Fair resource allocation design computation efficiency

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computation efficiency Maximization in Wireless-Powered Mobile Edge Computing Networks

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2020年第5期19卷 3170-3184页

作者： Zhou, Fuhui Hu, Rose Qingyang Nanjing Univ Aeronaut & Astronaut Coll Elect & Informat Engn Nanjing 210000 Peoples R China Nanjing Univ Aeronaut & Astronaut Key Lab Dynam Cognit Syst Electromagnet Spectrum Nanjing 210000 Peoples R China Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Utah State Univ Dept Elect & Comp Engn Logan UT 84322 USA

Energy-efficient computation is an inevitable trend for mobile edge computing (MEC) networks. Resource allocation strategies for maximizing the computation efficiency are critically important. In this paper, computation efficiency maximization problems are formulated in wireless-powered MEC networks under both partial and binary computation offloading modes. A practical non-linear energy harvesting model is considered. Both time division multiple access (TDMA) and non-orthogonal multiple access (NOMA) are considered and evaluated for offloading. The energy harvesting time, the local computing frequency, and the offloading time and power are jointly optimized to maximize the computation efficiency under the max-min fairness criterion. Two iterative algorithms and two alternative optimization algorithms are respectively proposed to address the non-convex problems formulated in this paper. Simulation results show that the proposed resource allocation schemes outperform the benchmark schemes in terms of user fairness. Moreover, a tradeoff is elucidated between the achievable computation efficiency and the total number of computed bits. Furthermore, simulation results demonstrate that the partial computation offloading mode outperforms the binary computation offloading mode and NOMA outperforms TDMA in terms of computation efficiency.

关键词： Mobile-edge computing wireless power transfer computation efficiency resource allocation binary computation offloading partial computation offloading

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computation efficiency Maximization for UAV-Assisted Relaying and MEC Networks in Urban Environment

IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING

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IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING 2023年第2期7卷 565-578页

作者： Wang, Longjie Zhou, Qiang Shen, Yan Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China

Unmanned aerial vehicle (UAV)-assisted relaying and mobile edge computing (MEC) networks is a popular and promising technology to provide high-quality computation service to ground users (GUs). However, prior works on UAV-assisted relaying and MEC networks do not consider the complicated channel conditions in urban environment. Furthermore, the optimization of computation efficiency, defined as the ratio of computation resources (in terms of CPU frequency) and energy consumption, is not studied in prior works. In this work, we consider an UAV-assisted relaying and MEC networks composed of GUs, two MEC servers carried by one rotary-wing UAV and one base station (BS), respectively. Meanwhile, we consider the probabilistic Line-of-Sight (LoS) channel model and the Rician fading model in urban environments to make the channel model more practical and accurate to urban environments. Further, we aim to solve the maximization problem of computation efficiency by jointly optimizing computation resources, computation offloading, bandwidth and UAV trajectory. For the non-convex formulated problem, we proposed an algorithm based on the Dinkelbach's method, the block coordinate descent (BCD) method and successive convex approximate (SCA) technique. Numerical results demonstrate that the proposed scheme can efficiently improve the computation efficiency compared to other traditional schemes in the practical simulation environment.

关键词： Unmanned aerial vehicle (UAV) probabilistic line-of-sight channel computation efficiency mobile edge computing (MEC)

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computation efficiency Maximization in Multi-UAV-Enabled Mobile Edge Computing Systems Based on 3D Deployment Optimization

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING 2023年第3期11卷 778-790页

作者： Deng, Xiaoheng Zhao, Jiahao Kuang, Zhufang Chen, Xuechen Guo, Qi Tang, Fengxiao Cent South Univ Res Inst Shenzhen Sch Comp Sci & Engn Changsha 410017 Hunan Peoples R China Cent South Univ Sch Comp Sci & Engn Changsha 410075 Peoples R China Cent South Univ Forestry & Technol Sch Comp & Informat Engn Changsha 410004 Peoples R China Jinchuan Nickel & Cobalt Res & Design Acad Jinchang 737100 Peoples R China

Unmanned aerial vehicles (UAVs) have been widely devoted to mobile edge computing (MEC) systems that have limited resources to provide high-quality computing and communication services for Internet of Things (IoT) terminals. Energy-efficient computation and resource allocation are key issues for the sustainable operation of the above-mentioned systems. The 3D deployment optimization of multi-UAVs is also crucial to maximizing the system's computation efficiency. In this paper, we discuss a multi-UAV-enabled MEC system. To maximize the computation efficiency of the terminal system, we consider jointly optimizing the terminal's CPU frequency, transmission power, offloading correlation decision, and the 3D position and beamwidth of the UAV. Since the original problem is a mixed-integer nonlinear programming (MINLP) problem with a fractional structure, which is difficult to solve directly. Based on Dinkelbach's method, convex optimization theory, and greedy strategy, we simplify the mathematical model and propose a four-stage alternating iterative computation efficiency maximization algorithm(FICEM) to solve the problem. The simulation results indicate that the algorithm converges fast in various network scenarios and that its computation efficiency is better than that of the baseline algorithm. In addition, the simulation results also manifest the effect of different network parameters on the computation efficiency of the terminal system.

关键词： Three-dimensional displays Resource management Optimization Task analysis Servers computational modeling Energy consumption 3D deployment optimization computation efficiency MEC multi -UAV resource allocation

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computation efficiency Maximization in OFDMA-Based Mobile Edge Computing Networks

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IEEE COMMUNICATIONS LETTERS 2020年第1期24卷 159-163页

作者： Wu, Yuhang Wang, Yuhao Zhou, Fuhui Qingyang Hu, Rose Nanchang Univ Sch Informat Engn Nanchang 330031 Jiangxi Peoples R China Utah State Univ Dept Elect & Comp Engn Logan UT 84322 USA

computation-efficient resource allocation strategies are of crucial importance in mobile edge computing networks. However, few works have focused on this issue. In this letter, weighted sum computation efficiency (CE) maximization problems are formulated in a mobile edge computing (MEC) network with orthogonal frequency division multiple access (OFDMA). Both partial offloading mode and binary offloading mode are considered. The closed-form expressions for the optimal subchannel and power allocation schemes are derived. In order to address the intractable non-convex weighted sum-of ratio problems, an efficiently iterative algorithm is proposed. Simulation results demonstrate that the CE achieved by our proposed resource allocation scheme is better than that obtained by the benchmark schemes.

关键词： Mobile edge computing computation efficiency resource allocation orthogonal frequency division multiple access

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computation efficiency Optimization for RIS-Assisted Millimeter-Wave Mobile Edge Computing Systems

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IEEE TRANSACTIONS ON COMMUNICATIONS 2022年第8期70卷 5528-5542页

作者： Yu, Xiangbin Yu, Kai Huang, Xu Dang, Xiaoyu Wang, Kezhi Cai, Jiali Nanjing Univ Aeronaut & Astronaut Coll Elect & Informat Engn Nanjing 210016 Peoples R China Southeast Univ State Key Lab Millimeter Waves Nanjing 211096 Peoples R China Northumbria Univ Dept Comp & Informat Sci Newcastle Upon Tyne NE1 8ST Tyne & Wear England

In this paper, we present the computation-efficient resource allocation (RA) schemes for millimeter-wave mobile edge computing (mmWave-MEC) system with the aid of reconfigurable intelligent surface (RIS), which is used to assist the uplink communication from the users to the base station (BS). By means of the theoretical analysis, the achievable rate and computation efficiency (CE) are derived. Then, the optimization problem for the CE maximization under the constraints of the minimum rate, maximum power consumption and local CPU frequency is formulated, where the joint design of the hybrid beamforming at the BS and the passive beamforming at the RIS as well as the local resource allocation of each user is carried out. An effective iterative algorithm based on the penalized inexact block coordinate descent (BCD) method is proposed to obtain the computation-efficient RA scheme. Next, a low-complexity suboptimal RA scheme based on the BCD method is proposed, and corresponding algorithm is presented. Simulation results show that the proposed schemes are effective, and high CE can be attained. Moreover, the second scheme can achieve the CE performance close to the first scheme but with lower complexity. Besides, it is effective to deploy the RIS scheme in mmWave-MEC system, which can strike a balance between the CE and energy consumption when compared to the conventional relay schemes.

关键词： Optimization Radio frequency Millimeter wave communication Resource management Wireless communication Simulation Array signal processing Millimeter-wave communication mobile edge computing reconfigurable intelligent surface computation efficiency hybrid beamforming

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computation efficiency Optimization for Millimeter-Wave Mobile Edge Computing Networks With NOMA

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2023年第8期22卷 4578-4593页

作者： Yu, Xiangbin Xu, Fangcheng Cai, Jiali Dang, Xiao-yu Wang, Kezhi Nanjing Univ Aeronaut & Astronaut Coll Elect & Informat Engn Nanjing 210016 Peoples R China Southeast Univ State Key Lab Millimeter Waves Nanjing 211189 Peoples R China Northumbria Univ Dept Comp & Informat Sci Newcastle Upon Tyne NE7 7YT England

In this paper, the millimeter-wave (mmWave) communications and non-orthogonal multiple access (NOMA) are exploited for mobile edge computing (MEC) networks to improve the performance of task offloading. Aiming at improving the computation efficiency (CE) and ensuring the fairness among users, we study the CE optimization for mmWave-MEC with NOMA, where both the analog beamforming (ABF) and hybrid beamforming (HBF) architectures under the partial offloading mode are considered. First, according to the max-min fairness criterion, the CE optimization problem is formulated to jointly optimize the ABF at the base station and the local resource allocation of each user in mmWave-MEC with ABF. An efficient algorithm based on the penalized successive convex approximation is proposed to solve this non-convex problem. Then, the max-min CE optimization problem in mmWave-MEC with HBF is studied, where the joint design of the HBF at the BS and the local resource allocation of each user is carried out. By using the penalty function and the inexact block coordinate descent method, a feasible optimization algorithm is developed to tackle this challenging problem. Simulation results verify the convergence of the proposed algorithms and show that the proposed resource allocation schemes can improve the system CE effectively, and the mmWave-MEC with HBF scheme can obtain higher CE than that with ABF scheme. Besides, the NOMA scheme exhibits superior performance over the conventional orthogonal multiple access scheme in terms of CE.

关键词： Mobile edge computing millimeter-wave communications non-or thogonal multiple access computation efficiency resource allocation

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computation efficiency in A Wireless-Powered Mobile Edge Computing Network with NOMA

Computation Efficiency in A Wireless-Powered Mobile Edge Com...

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IEEE International Conference on Communications (IEEE ICC)

作者： Zhou, Fuhui Wu, Yongpeng Hu, Rose Qingyang Qian, Yi Utah State Univ Logan UT 84322 USA Nanchang Univ Nanchang Jiangxi Peoples R China Shanghai Jiao Tong Univ Shanghai Peoples R China Univ Nebraska Lincoln NE 68583 USA

ISBN: (纸本)9781538680889

Energy-efficient computation is of crucial importance in mobile edge computing (MEC) networks. However, few investigations have studied resource allocation strategies for maximizing the computation efficiency. A computation efficiency maximization framework is established in wireless-powered MEC networks relying on non-orthogonal multiple access (NOMA) under both partial and binary computation offloading modes. A practical non-linear energy harvesting model is considered. The energy harvesting time, the local computing frequency, the operation mode selection, the offloading time and power are all jointly optimized to maximize the computation efficiency under the max-min fairness criterion. An iterative algorithm and an alternative optimization algorithm are proposed to solve the formulated challenging non-convex problems. Simulation results show that our proposed resource allocation schemes outperform the benchmark schemes in terms of computation efficiency. Moreover, a tradeoff is elucidated between the computation efficiency and the computation throughput.

关键词： Mobile edge computing non-orthogonal multiple access resource allocation computation efficiency wireless power transfer

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computation efficiency Maximization for Wireless-Powered Mobile Edge Computing

Computation Efficiency Maximization for Wireless-Powered Mob...

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IEEE Global Conference on Communications (GLOBECOM) / Workshop on Wireless Networking and Control for UAV

作者： Zhou, Fuhui Sun, Haijian Chu, Zheng Hu, Rose Qingyang Utah State Univ Logan UT 84322 USA Nanchang Univ Nanchang Jiangxi Peoples R China Univ Surrey Guildford Surrey England

ISBN: (纸本)9781538647271

Energy-efficient computation is an inevitable trend for mobile edge computing (MEC) networks. However, resource allocation strategies for maximizing the computation efficiency have not been fully investigated. In this paper a computation efficiency maximization problem is formulated in the wireless-powered MEC network under a practical non-linear energy harvesting model. The energy harvesting time, the local computing frequency, the offloading time, and power are all jointly optimized to maximize the computation efficiency under the max-min fairness criterion. The problem is non-convex and challenging to solve. An iterative algorithm is proposed to solve this problem. Simulation results show that our proposed resource allocation scheme outperforms the benchmark schemes in terms of the computation efficiency and verify the efficiency of our proposed algorithm. A tradeoff is elucidated between the achievable computation efficiency and the computation bits.

关键词： computation efficiency edge computing fairness resource allocation non-linear energy harvesting model

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computation efficiency Optimization for Active-RIS-Assisted Mobile Edge Computing Systems 23

Computation Efficiency Optimization for Active-RIS-Assisted ...

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23rd IEEE International Conference on Communication Technology, ICCT 2023

作者： Yu, Jiaxin Zhu, Jia Li, Yizhi Yang, Libao School of Telecomm. and Inform. Eng. Nanjing Univ. Posts & Telecomm. Nanjing China

ISBN: (纸本)9798350325959

This paper investigates an active reconfigurable intelligent surface (ARIS) assisted uplink mobile edge computing (MEC) systems. We propose an ARIS-aided user tasks computing scheme called ARIS-UTC, where a user simultaneously performs local computing and offloading computing to a multi-antenna access point (AP) assisted by an ARIS. To strike a balance between the data volume of computing tasks and energy consumption, we formulate a computation efficiency (CE) maximization problem for the proposed ARIS-UTC by jointly optimizing the user's transmit power, local computing frequency, ARIS reflection coefficients, and the receive beamforming vector at the AP. To handle the coupling variables and the non-convexity, the alternating optimization (AO) algorithm is utilized to separate the formulated problem into several subproblems, which are solved by using the semidefinite relaxation (SDR) together with the Dinkelbach methods. Numerical results demonstrate the effectiveness of the proposed AO based CE maximization algorithm. It is shown that the proposed ARIS-UTC outperforms the traditional passive reconfigurable intelligent surface-aided UTC (PRIS-UTC) as well as the non-RIS-aided UTC (nRIS-UTC) without the PRIS and ARIS in terms of the CE. © 2023 IEEE.

关键词： active reconfigurable intelligent surface computation efficiency mobile edge computing

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