6,742 research outputs found
End-to-end Throughput Maximization for Underlay Multi-hop Cognitive Radio Networks with RF Energy Harvesting
This paper studies a green paradigm for the underlay coexistence of primary
users (PUs) and secondary users (SUs) in energy harvesting cognitive radio
networks (EH-CRNs), wherein battery-free SUs capture both the spectrum and the
energy of PUs to enhance spectrum efficiency and green energy utilization. To
lower the transmit powers of SUs, we employ multi-hop transmission with time
division multiple access, by which SUs first harvest energy from the RF signals
of PUs and then transmit data in the allocated time concurrently with PUs, all
in the licensed spectrum. In this way, the available transmit energy of each SU
mainly depends on the harvested energy before the turn to transmit, namely
energy causality. Meanwhile, the transmit powers of SUs must be strictly
controlled to protect PUs from harmful interference. Thus, subject to the
energy causality constraint and the interference power constraint, we study the
end-to-end throughput maximization problem for optimal time and power
allocation. To solve this nonconvex problem, we first equivalently transform it
into a convex optimization problem and then propose the joint optimal time and
power allocation (JOTPA) algorithm that iteratively solves a series of
feasibility problems until convergence. Extensive simulations evaluate the
performance of EH-CRNs with JOTPA in three typical deployment scenarios and
validate the superiority of JOTPA by making comparisons with two other resource
allocation algorithms
Networked MIMO with Fractional Joint Transmission in Energy Harvesting Systems
This paper considers two base stations (BSs) powered by renewable energy
serving two users cooperatively. With different BS energy arrival rates, a
fractional joint transmission (JT) strategy is proposed, which divides each
transmission frame into two subframes. In the first subframe, one BS keeps
silent to store energy while the other transmits data, and then they perform
zero-forcing JT (ZF-JT) in the second subframe. We consider the average
sum-rate maximization problem by optimizing the energy allocation and the time
fraction of ZF-JT in two steps. Firstly, the sum-rate maximization for given
energy budget in each frame is analyzed. We prove that the optimal transmit
power can be derived in closed-form, and the optimal time fraction can be found
via bi-section search. Secondly, approximate dynamic programming (DP) algorithm
is introduced to determine the energy allocation among frames. We adopt a
linear approximation with the features associated with system states, and
determine the weights of features by simulation. We also operate the
approximation several times with random initial policy, named as policy
exploration, to broaden the policy search range. Numerical results show that
the proposed fractional JT greatly improves the performance. Also, appropriate
policy exploration is shown to perform close to the optimal.Comment: 33 pages, 7 figures, accepted by IEEE Transactions on Communication
Application Independent Energy Efficient Data Aggregation in Wireless Sensor Networks
Wireless Sensor networks are dense networks of small, low-cost sensors, which
collect and disseminate environmental data and thus facilitate monitoring and
controlling of physical environment from remote locations with better accuracy.
The major challenge is to achieve energy efficiency during the communication
among the nodes. This paper aims at proposing a solution to schedule the node's
activities to reduce the energy consumption. We propose the construction of a
decentralized lifetime maximizing tree within clusters. We aim at minimizing
the distance of transmission with minimization of energy consumption. The
sensor network is distributed into clusters based on the close proximity of the
nodes. Data transfer among the nodes is done with a hybrid technique of both
TDMA/ FDMA which leads to efficient utilization of bandwidth and maximizing
throughput.Comment: arXiv admin note: substantial text overlap with arXiv:1201.494
Optimum Transmission Policies for Battery Limited Energy Harvesting Nodes
Wireless networks with energy harvesting battery powered nodes are quickly
emerging as a viable option for future wireless networks with extended
lifetime. Equally important to their counterpart in the design of energy
harvesting radios are the design principles that this new networking paradigm
calls for. In particular, unlike wireless networks considered up to date, the
energy replenishment process and the storage constraints of the rechargeable
batteries need to be taken into account in designing efficient transmission
strategies. In this work, we consider such transmission policies for
rechargeable nodes, and identify the optimum solution for two related problems.
Specifically, the transmission policy that maximizes the short term throughput,
i.e., the amount of data transmitted in a finite time horizon is found. In
addition, we show the relation of this optimization problem to another, namely,
the minimization of the transmission completion time for a given amount of
data, and solve that as well. The transmission policies are identified under
the constraints on energy causality, i.e., energy replenishment process, as
well as the energy storage, i.e., battery capacity. The power-rate relationship
for this problem is assumed to be an increasing concave function, as dictated
by information theory. For battery replenishment, a model with discrete packets
of energy arrivals is considered. We derive the necessary conditions that the
throughput-optimal allocation satisfies, and then provide the algorithm that
finds the optimal transmission policy with respect to the short-term throughput
and the minimum transmission completion time. Numerical results are presented
to confirm the analytical findings.Comment: Submitted to IEEE Transactions on Wireless Communications, September
201
On Green Energy Powered Cognitive Radio Networks
Green energy powered cognitive radio (CR) network is capable of liberating
the wireless access networks from spectral and energy constraints. The
limitation of the spectrum is alleviated by exploiting cognitive networking in
which wireless nodes sense and utilize the spare spectrum for data
communications, while dependence on the traditional unsustainable energy is
assuaged by adopting energy harvesting (EH) through which green energy can be
harnessed to power wireless networks. Green energy powered CR increases the
network availability and thus extends emerging network applications. Designing
green CR networks is challenging. It requires not only the optimization of
dynamic spectrum access but also the optimal utilization of green energy. This
paper surveys the energy efficient cognitive radio techniques and the
optimization of green energy powered wireless networks. Existing works on
energy aware spectrum sensing, management, and sharing are investigated in
detail. The state of the art of the energy efficient CR based wireless access
network is discussed in various aspects such as relay and cooperative radio and
small cells. Envisioning green energy as an important energy resource in the
future, network performance highly depends on the dynamics of the available
spectrum and green energy. As compared with the traditional energy source, the
arrival rate of green energy, which highly depends on the environment of the
energy harvesters, is rather random and intermittent. To optimize and adapt the
usage of green energy according to the opportunistic spectrum availability, we
discuss research challenges in designing cognitive radio networks which are
powered by energy harvesters
Decentralized Lifetime Maximizing Tree with Clustering for Data Delivery in Wireless Sensor Networks
A wireless sensor network has a wide application domain which is expanding
everyday and they have been deployed pertaining to their application area. An
application independent approach is yet to come to terms with the ongoing
exploitation of the WSNs. In this paper we propose a decentralized lifetime
maximizing tree for application independent data aggregation scheme using the
clustering for data delivery in WSNs. The proposed tree will minimize the
energy consumption which has been a resisting factor in the smooth working of
WSNs as well as minimize the distance between the communicating nodes under the
control of a sub-sink which further communicate and transfer data to the sink
node.Comment: 9 pages, 8 figure
Vehicular Energy Network
The smart grid spawns many innovative ideas, but many of them cannot be
easily integrated into the existing power system due to power system
constraints, such as the lack of capacity to transport renewable energy in
remote areas to the urban centers. An energy delivery system can be built upon
the traffic network and electric vehicles (EVs) utilized as energy carriers to
transport energy over a large geographical region. A generalized architecture
called the vehicular energy network (VEN) is constructed and a mathematically
tractable framework is developed. Dynamic wireless (dis)charging allows
electric energy, as an energy packet, to be added and subtracted from EV
batteries seamlessly. With proper routing, energy can be transported from the
sources to destinations through EVs along appropriate vehicular routes. This
paper gives a preliminary study of VEN. Models are developed to study its
operational and economic feasibilities with real traffic data in the United
Kingdom. Our study shows that a substantial amount of renewable energy can be
transported from some remote wind farms to London under some reasonable
settings and VEN is likely to be profitable in the near future. VEN can
complement the power network and enhance its power delivery capability.Comment: 12 pages, accepted for publication in IEEE Transactions on
Transportation Electrificatio
Distributed Opportunistic Scheduling for Energy Harvesting Based Wireless Networks: A Two-Stage Probing Approach
This paper considers a heterogeneous ad hoc network with multiple
transmitter-receiver pairs, in which all transmitters are capable of harvesting
renewable energy from the environment and compete for one shared channel by
random access. In particular, we focus on two different scenarios: the constant
energy harvesting (EH) rate model where the EH rate remains constant within the
time of interest and the i.i.d. EH rate model where the EH rates are
independent and identically distributed across different contention slots. To
quantify the roles of both the energy state information (ESI) and the channel
state information (CSI), a distributed opportunistic scheduling (DOS) framework
with two-stage probing and save-then-transmit energy utilization is proposed.
Then, the optimal throughput and the optimal scheduling strategy are obtained
via one-dimension search, i.e., an iterative algorithm consisting of the
following two steps in each iteration: First, assuming that the stored energy
level at each transmitter is stationary with a given distribution, the expected
throughput maximization problem is formulated as an optimal stopping problem,
whose solution is proved to exist and then derived for both models; second, for
a fixed stopping rule, the energy level at each transmitter is shown to be
stationary and an efficient iterative algorithm is proposed to compute its
steady-state distribution. Finally, we validate our analysis by numerical
results and quantify the throughput gain compared with the best-effort delivery
scheme.Comment: 14 pages, 5 figures, accepted by IEEE/ACM Transactions on Networkin
Energy Efficiency in Massive MIMO-Based 5G Networks: Opportunities and Challenges
As we make progress towards the era of fifth generation (5G) communication
networks, energy efficiency (EE) becomes an important design criterion because
it guarantees sustainable evolution. In this regard, the massive multiple-input
multiple-output (MIMO) technology, where the base stations (BSs) are equipped
with a large number of antennas so as to achieve multiple orders of spectral
and energy efficiency gains, will be a key technology enabler for 5G. In this
article, we present a comprehensive discussion on state-of-the-art techniques
which further enhance the EE gains offered by massive MIMO (MM). We begin with
an overview of MM systems and discuss how realistic power consumption models
can be developed for these systems. Thereby, we discuss and identify few
shortcomings of some of the most prominent EE-maximization techniques present
in the current literature. Then, we discuss "hybrid MM systems" operating in a
5G architecture, where MM operates in conjunction with other potential
technology enablers, such as millimetre wave, heterogenous networks, and energy
harvesting networks. Multiple opportunities and challenges arise in such a 5G
architecture because these technologies benefit mutually from each other and
their coexistence introduces several new constraints on the design of
energy-efficient systems. Despite clear evidence that hybrid MM systems can
achieve significantly higher EE gains than conventional MM systems, several
open research problems continue to roadblock system designers from fully
harnessing the EE gains offered by hybrid MM systems. Our discussions lead to
the conclusion that hybrid MM systems offer a sustainable evolution towards 5G
networks and are therefore an important research topic for future work.Comment: IEEE Wireless Communications, under revie
Zero Energy Network stack for Energy Harvested WSNs
We present our ``Zero Energy Network'' (ZEN) protocol stack for energy
harvesting wireless sensor networks applications. The novelty in our work is
fold: (1) Energy harvesting aware fully featured MAC layer. Carrier
sensing, Backoff algorithms, ARQ, RTS/CTS mechanisms, Adaptive Duty Cycling are
either auto configurable or available as tunable parameters to match the
available energy (b) Energy harvesting aware Routing Protocol. The multi-hop
network establishes routes to the base station using a modified version of
AODVjr routing protocol assisted by energy predictions. (c) Application of a
time series called ``Holt-Winters'' for predicting the incoming energy. (d) A
distributed smart application running over the ZEN stack which utilizes a multi
parameter optimized perturbation technique to optimally use the available
energy. The application is capable of programming the ZEN stack in an energy
efficient manner. The energy harvested distributed smart application runs on a
realistic solar energy trace with a three year seasonality database. We
implement a smart application, capable of modifying itself to suit its own as
well as the network's energy level. Our analytical results show a close match
with the measurements conducted over EHWSN testbed.Comment: 12 pages, 201
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