898 research outputs found
Mobile Edge Computation Offloading Using Game Theory and Reinforcement Learning
Due to the ever-increasing popularity of resource-hungry and
delay-constrained mobile applications, the computation and storage capabilities
of remote cloud has partially migrated towards the mobile edge, giving rise to
the concept known as Mobile Edge Computing (MEC). While MEC servers enjoy the
close proximity to the end-users to provide services at reduced latency and
lower energy costs, they suffer from limitations in computational and radio
resources, which calls for fair efficient resource management in the MEC
servers. The problem is however challenging due to the ultra-high density,
distributed nature, and intrinsic randomness of next generation wireless
networks. In this article, we focus on the application of game theory and
reinforcement learning for efficient distributed resource management in MEC, in
particular, for computation offloading. We briefly review the cutting-edge
research and discuss future challenges. Furthermore, we develop a
game-theoretical model for energy-efficient distributed edge server activation
and study several learning techniques. Numerical results are provided to
illustrate the performance of these distributed learning techniques. Also, open
research issues in the context of resource management in MEC servers are
discussed
Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence
Along with the rapid developments in communication technologies and the surge
in the use of mobile devices, a brand-new computation paradigm, Edge Computing,
is surging in popularity. Meanwhile, Artificial Intelligence (AI) applications
are thriving with the breakthroughs in deep learning and the many improvements
in hardware architectures. Billions of data bytes, generated at the network
edge, put massive demands on data processing and structural optimization. Thus,
there exists a strong demand to integrate Edge Computing and AI, which gives
birth to Edge Intelligence. In this paper, we divide Edge Intelligence into AI
for edge (Intelligence-enabled Edge Computing) and AI on edge (Artificial
Intelligence on Edge). The former focuses on providing more optimal solutions
to key problems in Edge Computing with the help of popular and effective AI
technologies while the latter studies how to carry out the entire process of
building AI models, i.e., model training and inference, on the edge. This paper
provides insights into this new inter-disciplinary field from a broader
perspective. It discusses the core concepts and the research road-map, which
should provide the necessary background for potential future research
initiatives in Edge Intelligence.Comment: 13 pages, 3 figure
Extracting and Exploiting Inherent Sparsity for Efficient IoT Support in 5G: Challenges and Potential Solutions
Besides enabling an enhanced mobile broadband, next generation of mobile
networks (5G) are envisioned for the support of massive connectivity of
heterogeneous Internet of Things (IoT)s. These IoTs are envisioned for a large
number of use-cases including smart cities, environment monitoring, smart
vehicles, etc. Unfortunately, most IoTs have very limited computing and storage
capabilities and need cloud services. Hence, connecting these devices through
5G systems requires huge spectrum resources in addition to handling the massive
connectivity and improved security. This article discusses the challenges
facing the support of IoTs through 5G systems. The focus is devoted to
discussing physical layer limitations in terms of spectrum resources and radio
access channel connectivity. We show how sparsity can be exploited for
addressing these challenges especially in terms of enabling wideband spectrum
management and handling the connectivity by exploiting device-to-device
communications and edge-cloud. Moreover, we identify major open problems and
research directions that need to be explored towards enabling the support of
massive heterogeneous IoTs through 5G systems.Comment: Accepted for publication in IEEE Wireless Communications Magazin
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
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
Decentralized Computation Offloading for Multi-User Mobile Edge Computing: A Deep Reinforcement Learning Approach
Mobile edge computing (MEC) emerges recently as a promising solution to
relieve resource-limited mobile devices from computation-intensive tasks, which
enables devices to offload workloads to nearby MEC servers and improve the
quality of computation experience. Nevertheless, by considering a MEC system
consisting of multiple mobile users with stochastic task arrivals and wireless
channels in this paper, the design of computation offloading policies is
challenging to minimize the long-term average computation cost in terms of
power consumption and buffering delay. A deep reinforcement learning (DRL)
based decentralized dynamic computation offloading strategy is investigated to
build a scalable MEC system with limited feedback. Specifically, a continuous
action space-based DRL approach named deep deterministic policy gradient (DDPG)
is adopted to learn efficient computation offloading policies independently at
each mobile user. Thus, powers of both local execution and task offloading can
be adaptively allocated by the learned policies from each user's local
observation of the MEC system. Numerical results are illustrated to demonstrate
that efficient policies can be learned at each user, and performance of the
proposed DDPG based decentralized strategy outperforms the conventional deep
Q-network (DQN) based discrete power control strategy and some other greedy
strategies with reduced computation cost. Besides, the power-delay tradeoff is
also analyzed for both the DDPG based and DQN based strategies
Applications of Deep Reinforcement Learning in Communications and Networking: A Survey
This paper presents a comprehensive literature review on applications of deep
reinforcement learning in communications and networking. Modern networks, e.g.,
Internet of Things (IoT) and Unmanned Aerial Vehicle (UAV) networks, become
more decentralized and autonomous. In such networks, network entities need to
make decisions locally to maximize the network performance under uncertainty of
network environment. Reinforcement learning has been efficiently used to enable
the network entities to obtain the optimal policy including, e.g., decisions or
actions, given their states when the state and action spaces are small.
However, in complex and large-scale networks, the state and action spaces are
usually large, and the reinforcement learning may not be able to find the
optimal policy in reasonable time. Therefore, deep reinforcement learning, a
combination of reinforcement learning with deep learning, has been developed to
overcome the shortcomings. In this survey, we first give a tutorial of deep
reinforcement learning from fundamental concepts to advanced models. Then, we
review deep reinforcement learning approaches proposed to address emerging
issues in communications and networking. The issues include dynamic network
access, data rate control, wireless caching, data offloading, network security,
and connectivity preservation which are all important to next generation
networks such as 5G and beyond. Furthermore, we present applications of deep
reinforcement learning for traffic routing, resource sharing, and data
collection. Finally, we highlight important challenges, open issues, and future
research directions of applying deep reinforcement learning.Comment: 37 pages, 13 figures, 6 tables, 174 reference paper
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
A Survey on Mobile Edge Networks: Convergence of Computing, Caching and Communications
As the explosive growth of smart devices and the advent of many new
applications, traffic volume has been growing exponentially. The traditional
centralized network architecture cannot accommodate such user demands due to
heavy burden on the backhaul links and long latency. Therefore, new
architectures which bring network functions and contents to the network edge
are proposed, i.e., mobile edge computing and caching. Mobile edge networks
provide cloud computing and caching capabilities at the edge of cellular
networks. In this survey, we make an exhaustive review on the state-of-the-art
research efforts on mobile edge networks. We first give an overview of mobile
edge networks including definition, architecture and advantages. Next, a
comprehensive survey of issues on computing, caching and communication
techniques at the network edge is presented respectively. The applications and
use cases of mobile edge networks are discussed. Subsequently, the key enablers
of mobile edge networks such as cloud technology, SDN/NFV and smart devices are
discussed. Finally, open research challenges and future directions are
presented as well
Exploiting Non-Causal CPU-State Information for Energy-Efficient Mobile Cooperative Computing
Scavenging the idling computation resources at the enormous number of mobile
devices can provide a powerful platform for local mobile cloud computing. The
vision can be realized by peer-to-peer cooperative computing between edge
devices, referred to as co-computing. This paper considers a co-computing
system where a user offloads computation of input-data to a helper. The helper
controls the offloading process for the objective of minimizing the user's
energy consumption based on a predicted helper's CPU-idling profile that
specifies the amount of available computation resource for co-computing.
Consider the scenario that the user has one-shot input-data arrival and the
helper buffers offloaded bits. The problem for energy-efficient co-computing is
formulated as two sub-problems: the slave problem corresponding to adaptive
offloading and the master one to data partitioning. Given a fixed offloaded
data size, the adaptive offloading aims at minimizing the energy consumption
for offloading by controlling the offloading rate under the deadline and buffer
constraints. By deriving the necessary and sufficient conditions for the
optimal solution, we characterize the structure of the optimal policies and
propose algorithms for computing the policies. Furthermore, we show that the
problem of optimal data partitioning for offloading and local computing at the
user is convex, admitting a simple solution using the sub-gradient method.
Last, the developed design approach for co-computing is extended to the
scenario of bursty data arrivals at the user accounting for data causality
constraints. Simulation results verify the effectiveness of the proposed
algorithms.Comment: Submitted to possible journa
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