2,809 research outputs found
Finite Horizon Online Lazy Scheduling with Energy Harvesting Transmitters over Fading Channels
Lazy scheduling, i.e. setting transmit power and rate in response to data
traffic as low as possible so as to satisfy delay constraints, is a known
method for energy efficient transmission.This paper addresses an online lazy
scheduling problem over finite time-slotted transmission window and introduces
low-complexity heuristics which attain near-optimal performance.Particularly,
this paper generalizes lazy scheduling problem for energy harvesting systems to
deal with packet arrival, energy harvesting and time-varying channel processes
simultaneously. The time-slotted formulation of the problem and depiction of
its offline optimal solution provide explicit expressions allowing to derive
good online policies and algorithms
Energy Sharing for Multiple Sensor Nodes with Finite Buffers
We consider the problem of finding optimal energy sharing policies that
maximize the network performance of a system comprising of multiple sensor
nodes and a single energy harvesting (EH) source. Sensor nodes periodically
sense the random field and generate data, which is stored in the corresponding
data queues. The EH source harnesses energy from ambient energy sources and the
generated energy is stored in an energy buffer. Sensor nodes receive energy for
data transmission from the EH source. The EH source has to efficiently share
the stored energy among the nodes in order to minimize the long-run average
delay in data transmission. We formulate the problem of energy sharing between
the nodes in the framework of average cost infinite-horizon Markov decision
processes (MDPs). We develop efficient energy sharing algorithms, namely
Q-learning algorithm with exploration mechanisms based on the -greedy
method as well as upper confidence bound (UCB). We extend these algorithms by
incorporating state and action space aggregation to tackle state-action space
explosion in the MDP. We also develop a cross entropy based method that
incorporates policy parameterization in order to find near optimal energy
sharing policies. Through simulations, we show that our algorithms yield energy
sharing policies that outperform the heuristic greedy method.Comment: 38 pages, 10 figure
Capacity of Fading Gaussian Channel with an Energy Harvesting Sensor Node
Network life time maximization is becoming an important design goal in
wireless sensor networks. Energy harvesting has recently become a preferred
choice for achieving this goal as it provides near perpetual operation. We
study such a sensor node with an energy harvesting source and compare various
architectures by which the harvested energy is used. We find its Shannon
capacity when it is transmitting its observations over a fading AWGN channel
with perfect/no channel state information provided at the transmitter. We
obtain an achievable rate when there are inefficiencies in energy storage and
the capacity when energy is spent in activities other than transmission.Comment: 6 Pages, To be presented at IEEE GLOBECOM 201
EC-CENTRIC: An Energy- and Context-Centric Perspective on IoT Systems and Protocol Design
The radio transceiver of an IoT device is often where most of the energy is consumed. For this reason, most research so far has focused on low power circuit and energy efficient physical layer designs, with the goal of reducing the average energy per information bit required for communication. While these efforts are valuable per se, their actual effectiveness can be partially neutralized by ill-designed network, processing and resource management solutions, which can become a primary factor of performance degradation, in terms of throughput, responsiveness and energy efficiency. The objective of this paper is to describe an energy-centric and context-aware optimization framework that accounts for the energy impact of the fundamental functionalities of an IoT system and that proceeds along three main technical thrusts: 1) balancing signal-dependent processing techniques (compression and feature extraction) and communication tasks; 2) jointly designing channel access and routing protocols to maximize the network lifetime; 3) providing self-adaptability to different operating conditions through the adoption of suitable learning architectures and of flexible/reconfigurable algorithms and protocols. After discussing this framework, we present some preliminary results that validate the effectiveness of our proposed line of action, and show how the use of adaptive signal processing and channel access techniques allows an IoT network to dynamically tune lifetime for signal distortion, according to the requirements dictated by the application
Training Optimization for Energy Harvesting Communication Systems
Energy harvesting (EH) has recently emerged as an effective way to solve the
lifetime challenge of wireless sensor networks, as it can continuously harvest
energy from the environment. Unfortunately, it is challenging to guarantee a
satisfactory short-term performance in EH communication systems because the
harvested energy is sporadic. In this paper, we consider the channel training
optimization problem in EH communication systems, i.e., how to obtain accurate
channel state information to improve the communication performance. In contrast
to conventional communication systems, the optimization of the training power
and training period in EH communication systems is a coupled problem, which
makes such optimization very challenging. We shall formulate the optimal
training design problem for EH communication systems, and propose two solutions
that adaptively adjust the training period and power based on either the
instantaneous energy profile or the average energy harvesting rate. Numerical
and simulation results will show that training optimization is important in EH
communication systems. In particular, it will be shown that for short block
lengths, training optimization is critical. In contrast, for long block
lengths, the optimal training period is not too sensitive to the value of the
block length nor to the energy profile. Therefore, a properly selected fixed
training period value can be used.Comment: 6 pages, 5 figures, Globecom 201
Energy Harvesting Wireless Communications: A Review of Recent Advances
This article summarizes recent contributions in the broad area of energy
harvesting wireless communications. In particular, we provide the current state
of the art for wireless networks composed of energy harvesting nodes, starting
from the information-theoretic performance limits to transmission scheduling
policies and resource allocation, medium access and networking issues. The
emerging related area of energy transfer for self-sustaining energy harvesting
wireless networks is considered in detail covering both energy cooperation
aspects and simultaneous energy and information transfer. Various potential
models with energy harvesting nodes at different network scales are reviewed as
well as models for energy consumption at the nodes.Comment: To appear in the IEEE Journal of Selected Areas in Communications
(Special Issue: Wireless Communications Powered by Energy Harvesting and
Wireless Energy Transfer
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