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

In this paper, we consider a multi-user mobile edge computing (MEC) network powered by wireless power transfer (WPT), where each energy-harvesting WD follows a binary computation offloading policy, i.e., 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 multi-user computing mode selection and its strong coupling with 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 ADMM (alternating direction method of multipliers) decomposition technique, which enjoys much slower increase of computational complexity as the networks size increases. Extensive simulations show that both the proposed methods can efficiently achieve near-optimal performance under various network setups, and significantly outperform the other representative benchmark methods considered.Comment: This paper has been accepted for publication in IEEE Transactions on Wireless Communication

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

Mobile edge computing (MEC) and wireless power transfer (WPT) 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 outperforms other benchmark schemes. The results also demonstrate that the proposed schemes converge fast and have low computational complexity.Comment: This paper has been accepted by IEEE JSA

Multiuser Computation Offloading and Downloading for Edge Computing with Virtualization

Mobile-edge computing (MEC) is an emerging technology for enhancing the computational capabilities of mobile devices and reducing their energy consumption via offloading complex computation tasks to the nearby servers. Multiuser MEC at servers is widely realized via parallel computing based on virtualization. Due to finite shared I/O resources, interference between virtual machines (VMs), called I/O interference, degrades the computation performance. In this paper, we study the problem of joint radio-and-computation resource allocation (RCRA) in multiuser MEC systems in the presence of I/O interference. Specifically, offloading scheduling algorithms are designed targeting two system performance metrics: sum offloading throughput maximization and sum mobile energy consumption minimization. Their designs are formulated as non-convex mixed-integer programming problems, which account for latency due to offloading, result downloading and parallel computing. A set of low-complexity algorithms are designed based on a decomposition approach and leveraging classic techniques from combinatorial optimization. The resultant algorithms jointly schedule offloading users, control their offloading sizes, and divide time for communication (offloading and downloading) and computation. They are either optimal or can achieve close-to-optimality as shown by simulation. Comprehensive simulation results demonstrate considering of I/O interference can endow on an offloading controller robustness against the performance-degradation factor

Wireless Powered User Cooperative Computation in Mobile Edge Computing Systems

This paper studies a wireless powered mobile edge computing (MEC) system, where a dedicated energy transmitter (ET) uses the radio-frequency (RF) signal enabled wireless power transfer (WPT) to charge wireless devices for sustainable computation. In such a system, we present a new user cooperation approach to improve the computation performance of active devices, in which surrounding idle devices are enabled as helpers to use their opportunistically harvested wireless energy from the ET to help remotely execute active users' computation tasks. In particular, we consider a basic scenario with one user (with computation tasks to execute) and multiple helpers, in which the user can partition the computation tasks into various parts for local execution and computation offloading to helpers, respectively. Both the user and helpers are subject to the so-called energy neutrality constraints, such that their energy consumption does not exceed the respective energy harvested from the ET. Under this setup and considering a frequency division multiple access (FDMA) based computation offloading protocol, we maximize the computation rate (i.e., the number of computation bits over a particular time block) of the user, by jointly optimizing the transmit energy beamforming at the ET, as well as the communication and computation resource allocations at both the user and helpers. By leveraging the Lagrange duality method, we present the optimal solution to this problem in a semi-closed form. Numerical results show that the proposed wireless powered user cooperative computation design significantly improves the computation rate at the user, as compared to conventional schemes without such cooperation.Comment: 8 pages, 5 figures, accepted by Proc. IEEE GLOBECOM 2018 Workshop

Computation Efficiency Maximization in Wireless-Powered Mobile Edge Computing Networks

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.Comment: This paper has been accepted for publication in IEEE Transactions on Wireless Communication

Computation Rate Maximization for Wireless Powered Mobile Edge Computing

Integrating mobile edge computing (MEC) and wireless power transfer (WPT) has been regarded as a promising technique to improve computation capabilities for self-sustainable Internet of Things (IoT) devices. This paper investigates 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 for mobile computing. We consider a time-division multiple access (TDMA) protocol for multiuser computation offloading. Under this setup, we aim to maximize the weighted sum of the computation rates (in terms of the number of computation bits) across all the users, by jointly optimizing the energy transmit beamformer at the AP, the task partition for the users (for local computing and offloading, respectively), and the time allocation among the users. We derive the optimal solution in a semi-closed form via convex optimization techniques. Numerical results show the merit of the proposed design over alternative benchmark schemes.Comment: 6 pages and 2 figure

UAV-Enabled Mobile Edge Computing: Offloading Optimization and Trajectory Design

With the emergence of diverse mobile applications (such as augmented reality), the quality of experience of mobile users is greatly limited by their computation capacity and finite battery lifetime. Mobile edge computing (MEC) and wireless power transfer are promising to address this issue. However, these two techniques are susceptible to propagation delay and loss. Motivated by the chance of short-distance line-of-sight achieved by leveraging unmanned aerial vehicle (UAV) communications, an UAV-enabled wireless powered MEC system is studied. A power minimization problem is formulated subject to the constraints on the number of the computation bits and energy harvesting causality. The problem is non-convex and challenging to tackle. An alternative optimization algorithm is proposed based on sequential convex optimization. Simulation results show that our proposed design is superior to other benchmark schemes and the proposed algorithm is efficient in terms of the convergence.Comment: This paper has been accepted by IEEE ICC 201

Computation Efficiency Maximization in OFDMA-Based Mobile Edge Computing Networks

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.Comment: This paper has been accepted by IEEE Communications Letter

Decentralized Computation Offloading and Resource Allocation in Heterogeneous Networks with Mobile Edge Computing

We consider a heterogeneous network with mobile edge computing, where a user can offload its computation to one among multiple servers. In particular, we minimize the system-wide computation overhead by jointly optimizing the individual computation decisions, transmit power of the users, and computation resource at the servers. The crux of the problem lies in the combinatorial nature of multi-user offloading decisions, the complexity of the optimization objective, and the existence of inter-cell interference. Then, we decompose the underlying problem into two subproblems: i) the offloading decision, which includes two phases of user association and subchannel assignment, and ii) joint resource allocation, which can be further decomposed into the problems of transmit power and computation resource allocation. To enable distributed computation offloading, we sequentially apply a many-to-one matching game for user association and a one-to-one matching game for subchannel assignment. Moreover, the transmit power of offloading users is found using a bisection method with approximate inter-cell interference, and the computation resources allocated to offloading users is achieved via the duality approach. The proposed algorithm is shown to converge and is stable. Finally, we provide simulations to validate the performance of the proposed algorithm as well as comparisons with the existing frameworks.Comment: Submitted to IEEE Journa

Resource Allocation in Full-Duplex Mobile-Edge Computing Systems with NOMA and Energy Harvesting

This paper considers a full-duplex (FD) mobile-edge computing (MEC) system with non-orthogonal multiple access (NOMA) and energy harvesting (EH), where one group of users simultaneously offload task data to the base station (BS) via NOMA and the BS simultaneously receive data and broadcast energy to other group of users with FD. We aim at minimizing the total energy consumption of the system via power control, time scheduling and computation capacity allocation. To solve this nonconvex problem, we first transform it into an equivalent problem with less variables. The equivalent problem is shown to be convex in each vector with the other two vectors fixed, which allows us to design an iterative algorithm with low complexity. Simulation results show that the proposed algorithm achieves better performance than the conventional methods