1,895 research outputs found
Provisioning quality-of-service to energy harvesting wireless communications
Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Energy harvesting (EH) is an innovative way to build long-term and self-sustainable wireless networks. However, an inconstant EH rate may have an adverse effect on the quality-of-service (QoS) of wireless traffic, such as packet delay and error. In this article we discuss techniques that provide QoS to EH powered wireless communications. A new "dynamic string tautening" method is presented to produce the most energy efficient schedule with substantially lower complexity, compared to convex optimization techniques. The method adapts to the bursty arrivals of wireless traffic and harvested energy, and ensures that delay-sensitive data will be delivered by deadline. Comprehensive designs of EH powered transmitters are also discussed, where the EH rate, battery capacity, and deadline requirement can be jointly adjusted to leverage QoS and the cost.Peer reviewe
Energy-Efficient Optimization for Wireless Information and Power Transfer in Large-Scale MIMO Systems Employing Energy Beamforming
In this letter, we consider a large-scale multiple-input multiple-output
(MIMO) system where the receiver should harvest energy from the transmitter by
wireless power transfer to support its wireless information transmission. The
energy beamforming in the large-scale MIMO system is utilized to address the
challenging problem of long-distance wireless power transfer. Furthermore,
considering the limitation of the power in such a system, this letter focuses
on the maximization of the energy efficiency of information transmission (bit
per Joule) while satisfying the quality-of-service (QoS) requirement, i.e.
delay constraint, by jointly optimizing transfer duration and transmit power.
By solving the optimization problem, we derive an energy-efficient resource
allocation scheme. Numerical results validate the effectiveness of the proposed
scheme.Comment: 4 pages, 3 figures. IEEE Wireless Communications Letters 201
Online Learning for Offloading and Autoscaling in Energy Harvesting Mobile Edge Computing
Mobile edge computing (a.k.a. fog computing) has recently emerged to enable
in-situ processing of delay-sensitive applications at the edge of mobile
networks. Providing grid power supply in support of mobile edge computing,
however, is costly and even infeasible (in certain rugged or under-developed
areas), thus mandating on-site renewable energy as a major or even sole power
supply in increasingly many scenarios. Nonetheless, the high intermittency and
unpredictability of renewable energy make it very challenging to deliver a high
quality of service to users in energy harvesting mobile edge computing systems.
In this paper, we address the challenge of incorporating renewables into mobile
edge computing and propose an efficient reinforcement learning-based resource
management algorithm, which learns on-the-fly the optimal policy of dynamic
workload offloading (to the centralized cloud) and edge server provisioning to
minimize the long-term system cost (including both service delay and
operational cost). Our online learning algorithm uses a decomposition of the
(offline) value iteration and (online) reinforcement learning, thus achieving a
significant improvement of learning rate and run-time performance when compared
to standard reinforcement learning algorithms such as Q-learning. We prove the
convergence of the proposed algorithm and analytically show that the learned
policy has a simple monotone structure amenable to practical implementation.
Our simulation results validate the efficacy of our algorithm, which
significantly improves the edge computing performance compared to fixed or
myopic optimization schemes and conventional reinforcement learning algorithms.Comment: arXiv admin note: text overlap with arXiv:1701.01090 by other author
GreenDelivery: Proactive Content Caching and Push with Energy-Harvesting-based Small Cells
The explosive growth of mobile multimedia traffic calls for scalable wireless
access with high quality of service and low energy cost. Motivated by the
emerging energy harvesting communications, and the trend of caching multimedia
contents at the access edge and user terminals, we propose a paradigm-shift
framework, namely GreenDelivery, enabling efficient content delivery with
energy harvesting based small cells. To resolve the two-dimensional randomness
of energy harvesting and content request arrivals, proactive caching and push
are jointly optimized, with respect to the content popularity distribution and
battery states. We thus develop a novel way of understanding the interplay
between content and energy over time and space. Case studies are provided to
show the substantial reduction of macro BS activities, and thus the related
energy consumption from the power grid is reduced. Research issues of the
proposed GreenDelivery framework are also discussed.Comment: 15 pages, 5 figures, accepted by IEEE Communications Magazin
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
Wireless sensor networks monitor dynamic environments that change rapidly
over time. This dynamic behavior is either caused by external factors or
initiated by the system designers themselves. To adapt to such conditions,
sensor networks often adopt machine learning techniques to eliminate the need
for unnecessary redesign. Machine learning also inspires many practical
solutions that maximize resource utilization and prolong the lifespan of the
network. In this paper, we present an extensive literature review over the
period 2002-2013 of machine learning methods that were used to address common
issues in wireless sensor networks (WSNs). The advantages and disadvantages of
each proposed algorithm are evaluated against the corresponding problem. We
also provide a comparative guide to aid WSN designers in developing suitable
machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial
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