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computation Rate Maximization for Wireless Powered Mobile-Edge Computing With binary computation offloading

IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2018年第6期17卷 4177-4190页

作者： Bi, Suzhi Zhang, Ying Jun Shenzhen Univ Coll Informat Engn Shenzhen 518060 Peoples R China Chinese Univ Hong Kong Dept Informat Engn Hong Kong Hong Kong Peoples R China

Finite battery lifetime and low computing capability of size-constrained wireless devices (WDs) have been longstanding performance limitations of many low-power wireless networks, e.g., wireless sensor networks and Internet of Things. The recent development of radio frequency-based wireless power transfer (WPT) and mobile edge computing (MEC) technologies provide a promising solution to fully remove these limitations so as to achieve sustainable device operation and enhanced computational capability. In this paper, we consider a multi-user MEC network powered by the WPT, where each energy-harvesting WD follows a binary computation offloading policy, i.e., the data set of a task has to be executed as a whole either locally or remotely at the MEC server via task offloading. In particular, we are interested in maximizing the (weighted) sum computation rate of all the WDs in the network by jointly optimizing the individual computing mode selection (i.e., local computing or offloading) and the system transmission time allocation (on WPT and task offloading). The major difficulty lies in the combinatorial nature of the multi-user computing mode selection and its strong coupling with the transmission time allocation. To tackle this problem, we first consider a decoupled optimization, where we assume that the mode selection is given and propose a simple bi-section search algorithm to obtain the conditional optimal time allocation. On top of that, a coordinate descent method is devised to optimize the mode selection. The method is simple in implementation but may suffer from high computational complexity in a large-size network. To address this problem, we further propose a joint optimization method based on the alternating direction method of multipliers (ADMM) decomposition technique, which enjoys a much slower increase of computational complexity as the networks size increases. Extensive simulations show that both the proposed methods can efficiently achieve a near-optima

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

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computation Throughput Maximization for UAV-Enabled MEC with binary computation offloading

Computation Throughput Maximization for UAV-Enabled MEC with...

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IEEE International Conference on Communications (ICC)

作者： Xu, Changyuan Zhan, Cheng Liao, Jingrui Gong, Jue Southwest Univ Sch Comp & Informat Sci Chongqing Peoples R China

ISBN: (数字)9781538683477

ISBN: (纸本)9781538683477

Mobile edge computing (MEC) has been considered to provide computation services near the edge of mobile networks, while the unmanned aerial vehicle (UAV) is becoming an important integrated component to extend service coverage. In this paper, we consider a UAV-enabled MEC with binary computation offloading, where a UAV serves as an aerial edge server and each task of devices is either executing locally or offloading to the aerial edge server as a whole. To provide fairness among different ground devices, we aim to maximize the minimum computation throughput for all devices via the joint design of computing mode selection and UAV trajectory as well as resource allocation. The optimization problem is formulated as a mixed-integer nonlinear problem consisting of binary variables, which is difficult to tackle. The influence of non-binary solutions is penalized with a penalty function, based on which we develop an efficient iteration algorithm to obtain a suboptimal solution via leveraging the penalty successive convex approximation (P-SCA) method and difference of two convex (D.C.) optimization framework, where the algorithm is guaranteed to converge. Extensive simulations are conducted and the results with different system parameters show the effectiveness of the proposed joint design algorithm compared with other benchmark schemes.

关键词： unmanned aerial vehicle (UAV) mobile-edge computing (MEC) binary computation offloading penalty successive convex approximation (P-SCA)

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UAV-Enabled Mobile Edge Computing with binary computation offloading and Energy Constraints

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JOURNAL OF INTERNET TECHNOLOGY 2022年第5期23卷 947-954页

作者： Xu, Changyuan Zhan, Cheng Liao, Jingrui Zeng, Bin Southwest Univ Sch Comp & Informat Sci Chongqing Peoples R China

Mobile edge computing (MEC) has been considered to provide computation services near the edge of mobile networks, while the unmanned aerial vehicle (UAV) is becoming an important integrated component to extend service coverage. In this paper, we consider a UAV-enabled MEC with binary computation offloading and energy constraints, where an energy-limited UAV is employed as an aerial edge server and each task of devices is either executing locally or offloading to the aerial edge server as a whole. To provide fairness among different ground devices, we aim to maximize the minimum computation throughput among all devices via the joint design of computing mode selection and UAV trajectory as well as resource allocation. The optimization problem is formulated as a mixed-integer non-linear problem consisting of binary variables, which is difficult to tackle. By employing deductive penalty function to penalize the effect of non-binary solution, we develop an efficient iterative algorithm to obtain a suboptimal solution via leveraging the penalty successive convex approximation (P-SCA) method and difference of two convex (D.C.) optimization framework, where the algorithm is guaranteed to converge. Extensive simulations are conducted and the results with different system parameters show that the proposed joint design algorithm can improve the computation throughput by about 40% compared to other benchmark schemes.

关键词： Unmanned aerial vehicle (UAV) mobile-edge computing (MEC) binary computation offloading penalty successive convex approximation (P-SCA)

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Joint Energy-Based Optimization of binary offloading Decisions and Communication Resources in TDMA Systems, via Dynamic Programming

Joint Energy-Based Optimization of Binary Offloading Decisio...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Ji, Ruilin Dumitrescu, Sorina Department of Electrical and Computer Engineering McMaster University Hamilton Canada

ISBN: (纸本)9798350368741

This work addresses the problem of jointly optimizing the binary offloading decision making and the communication resource allocation for a system using time-division multiple access (TDMA). The goal is to minimize the cost of the energy expended by all devices, while meeting individual deadlines and power budget constraints. Prior work addressing this problem proposed a suboptimal solution. We show that the problem can be solved globally optimally using an efficient dynamic programming algorithm. © 2025 IEEE.

关键词： binary computation offloading dynamic programming resource allocation TDMA

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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 Rate Maximization in UAV-Enabled Wireless-Powered Mobile-Edge Computing Systems

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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2018年第9期36卷 1927-1941页

作者： Zhou, Fuhui Wu, Yongpeng Hu, Rose Qingyang Qian, Yi Nanchang Univ Sch Informat Engn Nanchang 330031 Jiangxi Peoples R China Utah State Univ Dept Elect & Comp Engn Logan UT 84322 USA Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Shanghai Jiao Tong Univ Dept Elect Engn Minhang 200240 Peoples R China Univ Nebraska Lincoln Dept Elect & Comp Engn Omaha NE 68182 USA

Mobile-edge computing (MEC) and wireless power transfer are two promising techniques to enhance the computation capability and to prolong the operational time of low-power wireless devices that are ubiquitous in Internet of Things. However, the computation performance and the harvested energy are significantly impacted by the severe propagation loss. In order to address this issue, an unmanned aerial vehicle (UAV)-enabled MEC wireless-powered system is studied in this paper. The computation rate maximization problems in a UAV-enabled MEC wireless powered system are investigated under both partial and binary computation offloading modes, subject to the energy-harvesting causal constraint and the UAV's speed constraint. These problems are non-convex and challenging to solve. A two-stage algorithm and a three-stage alternative algorithm are, respectively, proposed for solving the formulated problems. The closed-form expressions for the optimal central processing unit frequencies, user offloading time, and user transmit power are derived. The optimal selection scheme on whether users choose to locally compute or offload computation tasks is proposed for the binary computation offloading mode. Simulation results show that our proposed resource allocation schemes outperform other benchmark schemes. The results also demonstrate that the proposed schemes converge fast and have low computational complexity.

关键词： Mobile-edge computing wireless power transfer unmanned aerial vehicle-enabled resource allocation binary computation offloading partial computation offloading

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A Reinforcement Model Based Prioritized Replay to Solve the offloading Problem in Edge Computing 16th

A Reinforcement Model Based Prioritized Replay to Solve the ...

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16th International Conference on Wireless Algorithms, Systems, and Applications (WASA)

作者： Liu, Wenrui Wang, Cong Mi, Jiazhi Luan, He Luo, Yixin Northeastern Univ Qinhuangdao Qinhuangdao Hebei Peoples R China

ISBN: (纸本)9783030861377;9783030861360

Mobile edge computing is widely applied to help mobile devices to improve its data processing speed. However, one of its main challenges is how to generate computation offloading decision effectively and quickly in the complex wireless scenario. In this paper, we aim to build up a multiple user devices application scenario, where each device performs binary computation offloading policy which is executed in the local device or offloaded to a cloud server via the wireless network. A model based on deep reinforcement learning is proposed to optimize computation offloading decisions. First, the weighted rate of offloading computation is introduced to be a reward in the Q function. Second, offloading decisions are generated from a deep Q-network (DQN) with batch normalization layers. At last, the deep Q-network is trained with a designed prioritized replay policy. Experimental results indicate the proposed model generates the optimal offloading decisions in a short time and gets faster convergence speed on the weighted rate.

关键词： Mobile edge computing binary computation offloading Deep reinforcement learning Prioritized replay

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