Joint offloading and computing optimization in wireless powered mobile-edge computing systems

By Feng Wang, Jie Xu, Xin Wang, Shuguang Cui. 2017 IEEE International Conference on Communications (ICC), 2017

680 citations recorded by the source corpus.

Integrating mobile-edge computing (MEC) and wireless power transfer (WPT) is a promising technique in the Internet of Things (IoT) era. It can provide massive low-power mobile devices with enhanced computation capability and sustainable energy supply. In this paper, we consider a wireless powered multiuser MEC system, where a multi-antenna access point (AP) (integrated with an MEC server) broadcasts wireless power to charge multiple users and each user node relies on the harvested energy to execute latency-sensitive computation tasks. With MEC, these users can execute their respective tasks locally by themselves or offload all or part of the tasks to the AP based on a time division multiple access (TDMA) protocol. Under this setup, we pursue an energy-efficient MEC-WPT system design by jointly optimizing the transmit energy beamformer at the AP, the central processing unit (CPU) frequencies and the offloaded bits at each user, as well as the time allocation among different users. In particular, we minimize the energy consumption at the AP over a particular time block subject to the computation latency and energy harvesting constraints per user. By formulating this problem into a convex framework and employing the Lagrange duality method, we obtain its optimal solution in a semi-closed form. Numerical results demonstrate the merits of the proposed joint design over alternative benchmark schemes.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/e4819306fa1cdf8e4da622222cb18a833c8148e4

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Citations in the corpus

Computation Rate Maximization in UAV-Enabled Wireless-Powered Mobile-Edge Computing Systems (2018, methodology): “The simulation settings are based on the works in [7], [14], [16], and [23].”
A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art (2019, methodology): “The impact of energy transmit beamforming technique in MEC system was investigated in [147] where an innovative framework to minimize the”
UAV-Assisted Relaying and Edge Computing: Scheduling and Trajectory Optimization (2018, background): “In addition, the works in [11]–[14] introduced the use of energy harvesting or wireless power transfer (WPT) technologies into the cellular-based MEC systems, which has enabled the UEs to have sustainable energy support to their transmissions and computation, but at the cost of increasing the comput”
Intelligent Reflecting Surface Aided MIMO Broadcasting for Simultaneous Wireless Information and Power Transfer (2019, methodology): “Hence, for simplicity, we adopt the simple linear EH model as widely used in the existing literature [29], [37], [38].”
Energy-Efficient UAV-Assisted Mobile Edge Computing: Resource Allocation and Trajectory Optimization (2020, methodology): “Full granularity in task partition is considered, where the task-input data can be arbitrarily divided for local and remote executions [25], [28], [29].”
Throughput Maximization for UAV-Enabled Wireless Powered Communication Networks (2018, background): “However, in conventional WPCNs, access points (APs) are usually deployed at fixed locations, which cannot be changed once deployed [8]–[10].”
Joint Load Balancing and Offloading in Vehicular Edge Computing and Networks (2019, background): “Recent works on MEC include those that reduce energy consumption [7], [8], [10], [12], [14], [15], and those that reduce latency [16], [17] by offloading computation-heavy and latency-sensitive tasks to nearby RSUs and/or base stations for remote execution.”
Learning-Based Computation Offloading for IoT Devices With Energy Harvesting (2017, background): “This is further complicated by the chosen edge device carrying out heavy workloads and experiencing degraded radio channel fading and interference [9].”
Joint computation and communication cooperation for mobile edge computing (2017, background): “Furthermore, [9], [10] proposed interesting wireless powered MEC systems by integrating the emerging wireless power transfer (WPT) technique into MEC, in order to achieve self-sustainable mobile computing.”
A Comprehensive Overview on 5G-and-Beyond Networks With UAVs: From Communications to Sensing and Intelligence (2020, background): “For instance, the authors in [314] presented a UAV-relayassisted MEC system by integrating UAVs into the cooperative MEC design [324], where the UAV not only acts as the MEC server for remote task execution, but also serves as a relay to help ground devices offload tasks to another GBS.”
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Optimized Computation Offloading Performance in Virtual Edge Computing Systems Via Deep Reinforcement Learning (2018, methodology): “[9] leveraged a Lagrangian duality method to minimize the total energy consumption in a computation latency constrained wireless powered multiuser MEC system.”
Computation Rate Maximization for Wireless Powered Mobile-Edge Computing With Binary Computation Offloading (2017, background): “In a multi-user scenario, [19] considers using a multi-antenna AP to power the users and minimizes the AP’s total energy consumption subject to the users’ individual latency constraints.”