2,333 research outputs found
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
Energy-Efficient Joint Offloading and Wireless Resource Allocation Strategy in Multi-MEC Server Systems
Mobile edge computing (MEC) is an emerging paradigm that mobile devices can
offload the computation-intensive or latency-critical tasks to the nearby MEC
servers, so as to save energy and extend battery life. Unlike the cloud server,
MEC server is a small-scale data center deployed at a wireless access point,
thus it is highly sensitive to both radio and computing resource. In this
paper, we consider an Orthogonal Frequency-Division Multiplexing Access (OFDMA)
based multi-user and multi-MEC-server system, where the task offloading
strategies and wireless resources allocation are jointly investigated. Aiming
at minimizing the total energy consumption, we propose the joint offloading and
resource allocation strategy for latency-critical applications. Through the
bi-level optimization approach, the original NP-hard problem is decoupled into
the lower-level problem seeking for the allocation of power and subcarrier and
the upper-level task offloading problem. Simulation results show that the
proposed algorithm achieves excellent performance in energy saving and
successful offloading probability (SOP) in comparison with conventional
schemes.Comment: 6 pages, 5 figures, to appear in IEEE ICC 2018, May 20-2
Optimal Task Offloading and Resource Allocation in Mobile-Edge Computing with Inter-user Task Dependency
Mobile-edge computing (MEC) has recently emerged as a cost-effective paradigm
to enhance the computing capability of hardware-constrained wireless devices
(WDs). In this paper, we first consider a two-user MEC network, where each WD
has a sequence of tasks to execute. In particular, we consider task dependency
between the two WDs, where the input of a task at one WD requires the final
task output at the other WD. Under the considered task-dependency model, we
study the optimal task offloading policy and resource allocation (e.g., on
offloading transmit power and local CPU frequencies) that minimize the weighted
sum of the WDs' energy consumption and task execution time. The problem is
challenging due to the combinatorial nature of the offloading decisions among
all tasks and the strong coupling with resource allocation. To tackle this
problem, we first assume that the offloading decisions are given and derive the
closed-form expressions of the optimal offloading transmit power and local CPU
frequencies. Then, an efficient bi-section search method is proposed to obtain
the optimal solutions. Furthermore, we prove that the optimal offloading
decisions follow an one-climb policy, based on which a reduced-complexity Gibbs
Sampling algorithm is proposed to obtain the optimal offloading decisions. We
then extend the investigation to a general multi-user scenario, where the input
of a task at one WD requires the final task outputs from multiple other WDs.
Numerical results show that the proposed method can significantly outperform
the other representative benchmarks and efficiently achieve low complexity with
respect to the call graph size.Comment: This paper has been accepted for publication in IEEE Transactions on
Wireless Communication
Exploiting Computation Replication for Mobile Edge Computing: A Fundamental Computation-Communication Tradeoff Study
Existing works on task offloading in mobile edge computing (MEC) networks
often assume a task is executed once at a single edge node (EN). Downloading
the computed result from the EN back to the mobile user may suffer long delay
if the downlink channel experiences strong interference or deep fading. This
paper exploits the idea of computation replication in MEC networks to speed up
the downloading phase. Computation replication allows each user to offload its
task to multiple ENs for repetitive execution so as to create multiple copies
of the computed result at different ENs which can then enable transmission
cooperation and hence reduce the communication latency for result downloading.
Yet, computation replication may also increase the communication latency for
task uploading, despite the obvious increase in computation load. The main
contribution of this work is to characterize asymptotically an order-optimal
upload-download communication latency pair for a given computation load in a
multi-user multi-server MEC network. Analysis shows when the computation load
increases within a certain range, the downloading time decreases in an
inversely proportional way if it is binary offloading or decreases linearly if
it is partial offloading, both at the expense of linear increase in the
uploading time.Comment: To appear in IEEE Transactions on Wireless Communication
Base Station ON-OFF Switching in 5G Wireless Networks: Approaches and Challenges
To achieve the expected 1000x data rates under the exponential growth of
traffic demand, a large number of base stations (BS) or access points (AP) will
be deployed in the fifth generation (5G) wireless systems, to support high data
rate services and to provide seamless coverage. Although such BSs are expected
to be small-scale with lower power, the aggregated energy consumption of all
BSs would be remarkable, resulting in increased environmental and economic
concerns. In existing cellular networks, turning off the under-utilized BSs is
an efficient approach to conserve energy while preserving the quality of
service (QoS) of mobile users. However, in 5G systems with new physical layer
techniques and the highly heterogeneous network architecture, new challenges
arise in the design of BS ON-OFF switching strategies. In this article, we
begin with a discussion on the inherent technical challenges of BS ON-OFF
switching. We then provide a comprehensive review of recent advances on
switching mechanisms in different application scenarios. Finally, we present
open research problems and conclude the paper.Comment: Appear to IEEE Wireless Communications, 201
Multi-Antenna NOMA for Computation Offloading in Multiuser Mobile Edge Computing Systems
This paper studies a multiuser mobile edge computing (MEC) system, in which
one base station (BS) serves multiple users with intensive computation tasks.
We exploit the multi-antenna non-orthogonal multiple access (NOMA) technique
for multiuser computation offloading, such that different users can
simultaneously offload their computation tasks to the multi-antenna BS over the
same time/frequency resources, and the BS can employ successive interference
cancellation (SIC) to efficiently decode all users' offloaded tasks for remote
execution. We aim to minimize the weighted sum-energy consumption at all users
subject to their computation latency constraints, by jointly optimizing the
communication and computation resource allocation as well as the BS's decoding
order for SIC. For the case with partial offloading, the weighted sum-energy
minimization is a convex optimization problem, for which an efficient algorithm
based on the Lagrange duality method is presented to obtain the globally
optimal solution. For the case with binary offloading, the weighted sum-energy
minimization corresponds to a {\em mixed Boolean convex problem} that is
generally more difficult to be solved. We first use the branch-and-bound (BnB)
method to obtain the globally optimal solution, and then develop two
low-complexity algorithms based on the greedy method and the convex relaxation,
respectively, to find suboptimal solutions with high quality in practice. Via
numerical results, it is shown that the proposed NOMA-based computation
offloading design significantly improves the energy efficiency of the multiuser
MEC system as compared to other benchmark schemes. It is also shown that for
the case with binary offloading, the proposed greedy method performs close to
the optimal BnB based solution, and the convex relaxation based solution
achieves a suboptimal performance but with lower implementation complexity.Comment: 33 pages, 12 figures, as well as correcting the typos in equations
(4) and (5) in the previous versio
Fundamental Green Tradeoffs: Progresses, Challenges, and Impacts on 5G Networks
With years of tremendous traffic and energy consumption growth, green radio
has been valued not only for theoretical research interests but also for the
operational expenditure reduction and the sustainable development of wireless
communications. Fundamental green tradeoffs, served as an important framework
for analysis, include four basic relationships: spectrum efficiency (SE) versus
energy efficiency (EE), deployment efficiency (DE) versus energy efficiency
(EE), delay (DL) versus power (PW), and bandwidth (BW) versus power (PW). In
this paper, we first provide a comprehensive overview on the extensive on-going
research efforts and categorize them based on the fundamental green tradeoffs.
We will then focus on research progresses of 4G and 5G communications, such as
orthogonal frequency division multiplexing (OFDM) and non-orthogonal
aggregation (NOA), multiple input multiple output (MIMO), and heterogeneous
networks (HetNets). We will also discuss potential challenges and impacts of
fundamental green tradeoffs, to shed some light on the energy efficient
research and design for future wireless networks.Comment: revised from IEEE Communications Surveys & Tutorial
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
A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems
The ongoing deployment of 5G cellular systems is continuously exposing the
inherent limitations of this system, compared to its original premise as an
enabler for Internet of Everything applications. These 5G drawbacks are
currently spurring worldwide activities focused on defining the next-generation
6G wireless system that can truly integrate far-reaching applications ranging
from autonomous systems to extended reality and haptics. Despite recent 6G
initiatives1, the fundamental architectural and performance components of the
system remain largely undefined. In this paper, we present a holistic,
forward-looking vision that defines the tenets of a 6G system. We opine that 6G
will not be a mere exploration of more spectrum at high-frequency bands, but it
will rather be a convergence of upcoming technological trends driven by
exciting, underlying services. In this regard, we first identify the primary
drivers of 6G systems, in terms of applications and accompanying technological
trends. Then, we propose a new set of service classes and expose their target
6G performance requirements. We then identify the enabling technologies for the
introduced 6G services and outline a comprehensive research agenda that
leverages those technologies. We conclude by providing concrete recommendations
for the roadmap toward 6G. Ultimately, the intent of this article is to serve
as a basis for stimulating more out-of-the-box research around 6G.Comment: This paper has been accepted by IEEE Networ
Hierarchical Fog-Cloud Computing for IoT Systems: A Computation Offloading Game
Fog computing, which provides low-latency computing services at the network
edge, is an enabler for the emerging Internet of Things (IoT) systems. In this
paper, we study the allocation of fog computing resources to the IoT users in a
hierarchical computing paradigm including fog and remote cloud computing
services. We formulate a computation offloading game to model the competition
between IoT users and allocate the limited processing power of fog nodes
efficiently. Each user aims to maximize its own quality of experience (QoE),
which reflects its satisfaction of using computing services in terms of the
reduction in computation energy and delay. Utilizing a potential game approach,
we prove the existence of a pure Nash equilibrium and provide an upper bound
for the price of anarchy. Since the time complexity to reach the equilibrium
increases exponentially in the number of users, we further propose a
near-optimal resource allocation mechanism and prove that in a system with
IoT users, it can achieve an -Nash equilibrium in
time. Through numerical studies, we evaluate the users' QoE as well as the
equilibrium efficiency. Our results reveal that by utilizing the proposed
mechanism, more users benefit from computing services in comparison to an
existing offloading mechanism. We further show that our proposed mechanism
significantly reduces the computation delay and enables low-latency fog
computing services for delay-sensitive IoT applications
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