9 research outputs found
Optimal Energy Management for Energy Harvesting Transmitter and Receiver with Helper
We study energy harvesting (EH) transmitter and receiver, where the receiver
decodes data using the harvested energy from the nature and from an independent
EH node, named helper. Helper cooperates with the receiver by transferring its
harvested energy to the receiver over an orthogonal fading channel. We study an
offline optimal power management policy to maximize the reliable information
rate. The harvested energy in all three nodes are assumed to be known. We
consider four different scenarios; First, for the case that both transmitter
and the receiver have batteries, we show that the optimal policy is
transferring the helper harvested energy to the receiver, immediately. Next,
for the case of non-battery receiver and full power transmitter, we model a
virtual EH receiver with minimum energy constraint to achieve an optimal
policy. Then, we consider a non-battery EH receiver and EH transmitter with
battery. Finally, we derive optimal power management wherein neither the
transmitter nor the receiver have batteries. We propose three iterative
algorithms to compute optimal energy management policies. Numerical results are
presented to corroborate the advantage of employing the helper.Comment: It is a conference paper with 5 pages and one figure, submitted to
ISITA201
Joint Transmission and Energy Transfer Policies for Energy Harvesting Devices with Finite Batteries
One of the main concerns in traditional Wireless Sensor Networks (WSNs) is
energy efficiency. In this work, we analyze two techniques that can extend
network lifetime. The first is Ambient \emph{Energy Harvesting} (EH), i.e., the
capability of the devices to gather energy from the environment, whereas the
second is Wireless \emph{Energy Transfer} (ET), that can be used to exchange
energy among devices. We study the combination of these techniques, showing
that they can be used jointly to improve the system performance. We consider a
transmitter-receiver pair, showing how the ET improvement depends upon the
statistics of the energy arrivals and the energy consumption of the devices.
With the aim of maximizing a reward function, e.g., the average transmission
rate, we find performance upper bounds with and without ET, define both online
and offline optimization problems, and present results based on realistic
energy arrivals in indoor and outdoor environments. We show that ET can
significantly improve the system performance even when a sizable fraction of
the transmitted energy is wasted and that, in some scenarios, the online
approach can obtain close to optimal performance.Comment: 16 pages, 12 figure
Incentivizing Signal and Energy Cooperation in Wireless Networks
Abstract-We consider a two-hop wireless network where the source(s) in the network have the ability to wirelessly power the relay(s) who also have their own data to send to the destination. Considering the fact that each node in the network aims to maximize its own metric, we adopt a game theoretic approach that foresees offering relaying of the sources' data in exchange for energy provided to the relays, and simultaneously offering energy to the relays in exchange for their relaying services. We first study a Stackelberg competition with the single relay node as the leader, and investigate the impact of having multiple source nodes in the system. We next study the reciprocal Stackelberg game with the single source as the leader, and investigate the inter-relay competition with multiple relays. We find that in the Stackelberg games, the leader can improve its individual utility by influencing the follower's decision accordingly, even more so when there are multiple followers. We next formulate a noncooperative game between the source and the relay and show the existence of a unique Nash equilibrium by an appropriate pricing mechanism. The equilibrium maximizes the total utility of the network and allows the destination to choose how much data to receive from each node
Stochastic Optimization of Energy Harvesting Wireless Communication Networks
Energy harvesting from environmental energy sources (e.g., sunlight) or from man-made
sources (e.g., RF energy) has been a game-changing paradigm, which enabled the possibility
of making the devices in the Internet of Things or wireless sensor networks operate
autonomously and with high performance for years or even decades without human
intervention. However, an energy harvesting system must be correctly designed to achieve
such a goal and therefore the energy management problem has arisen and become a critical
aspect to consider in modern wireless networks. In particular, in addition to the hardware
(e.g., in terms of circuitry design) and application point of views (e.g., sensor deployment),
also the communication protocol perspective must be explicitly taken into account; indeed,
the use of the wireless communication interface may play a dominant role in the energy
consumption of the devices, and thus must be correctly designed and optimized. This
analysis represents the focus of this thesis.
Energy harvesting for wireless system has been a very active research topic in the past
decade. However, there are still many aspects that have been neglected or not completely
analyzed in the literature so far. Our goal is to address and solve some of these new
problems using a common stochastic optimization setup based on dynamic programming.
In particular, we formulate both the centralized and decentralized optimization problems
in an energy harvesting network with multiple devices, and discuss the interrelations
between these two schemes; we study the combination of environmental energy harvesting
and wireless energy transfer to improve the transmission rate of the network and achieve a
balanced situation; we investigate the long-term optimization problem in wireless powered
communication networks, in which the receiver supplies wireless energy to the terminal
nodes; we deal with the energy storage inefficiencies of the energy harvesting devices,
and show that traditional policies may be strongly suboptimal in this context; finally, we
investigate how it is possible to increase secrecy in a wireless link where a third malicious
party eavesdrops the information transmitted by an energy harvesting node