Computation Rate Maximization for Wireless Powered Mobile-Edge Computing With Binary Computation Offloading

By Suzhi Bi, Ying-Jun Angela Zhang. IEEE Transactions on Wireless Communications, 2017

771 citations recorded by the source corpus.

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-optimal performance under various network setups, and significantly outperform the other representative benchmark methods considered.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/9672c265c286494936a3175c0df85a32c1b36985

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Citations in the corpus

Learning-Based Computation Offloading for IoT Devices With Energy Harvesting (2017, methodology): “Furthermore, a binary offloading policy in a wireless-powered MEC network is proposed, which utilizes the AP to transfer RF power to the mobile device and execute the computation tasks [12].”
Edge-Computing-Enabled Smart Cities: A Comprehensive Survey (2019, neutral): “A user offloads either all components of an application (binary offloading) [103]–[105] or some components of an application to the edge server (partial”
A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art (2019, neutral): “Considering the WPT-enabled MEC systems, [114] aimed to maximize the (weighted) sum computation rate of all wireless devices in the network by jointly optimizing the individual offloading decision and the time allocation for transmission.”
Computation Rate Maximization in UAV-Enabled Wireless-Powered Mobile-Edge Computing Systems (2018, methodology): “Similar to [13]–[16], each user can simultaneously perform energy harvesting, local computing and computation offloading while the UAV can simultaneously transmit energy and perform computation.”
Deep Reinforcement Learning for Task Offloading in Mobile Edge Computing Systems (2020, methodology): “Bi et al. in [5] focused on a wireless-powered MEC scenario and proposed an algorithm to jointly optimize the ofﬂoading and power transfer decisions.”
A Survey on End-Edge-Cloud Orchestrated Network Computing Paradigms (2019, background): “Reference [20] considers the case where the MDs are powered by wireless energy transferred from the edge servers; the authors schedule the time allocation between energy transfer and computation task offloading to maximize the computation rate, i.e., the ratio between processed data and the…”
Deep Reinforcement Learning for Online Computation Offloading in Wireless Powered Mobile-Edge Computing Networks (2018, methodology): “For instance, meta-heuristic search algorithms are proposed in [7] and [12] to optimize the ofﬂoading decisions.”
Energy Efficient Resource Allocation in UAV-Enabled Mobile Edge Computing Networks (2019, methodology): “In binary computation offloading, the computation tasks are either locally executed or offloaded to MEC servers [35], [36].”
Joint Computation and Communication Cooperation for Energy-Efficient Mobile Edge Computing (2018, methodology): “Furthermore, [15]–[17] proposed wireless powered MEC systems by integrating the emerging wireless power transfer (WPT) technology into MEC for self-sustainable computing, where the AP employs WPT to power the users’ local computing and offloading.”
Resource Scheduling in Edge Computing: A Survey (2021, background): “energy-harvesting or wireless-charging enabled edge computing [79], [111].”