2,834 research outputs found
Information-Theoretic Analysis of an Energy Harvesting Communication System
In energy harvesting communication systems, an exogenous recharge process
supplies energy for the data transmission and arriving energy can be buffered
in a battery before consumption. Transmission is interrupted if there is not
sufficient energy. We address communication with such random energy arrivals in
an information-theoretic setting. Based on the classical additive white
Gaussian noise (AWGN) channel model, we study the coding problem with random
energy arrivals at the transmitter. We show that the capacity of the AWGN
channel with stochastic energy arrivals is equal to the capacity with an
average power constraint equal to the average recharge rate. We provide two
different capacity achieving schemes: {\it save-and-transmit} and {\it
best-effort-transmit}. Next, we consider the case where energy arrivals have
time-varying average in a larger time scale. We derive the optimal offline
power allocation for maximum average throughput and provide an algorithm that
finds the optimal power allocation.Comment: Published in IEEE PIMRC, September 201
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
RF-Powered Cognitive Radio Networks: Technical Challenges and Limitations
The increasing demand for spectral and energy efficient communication
networks has spurred a great interest in energy harvesting (EH) cognitive radio
networks (CRNs). Such a revolutionary technology represents a paradigm shift in
the development of wireless networks, as it can simultaneously enable the
efficient use of the available spectrum and the exploitation of radio frequency
(RF) energy in order to reduce the reliance on traditional energy sources. This
is mainly triggered by the recent advancements in microelectronics that puts
forward RF energy harvesting as a plausible technique in the near future. On
the other hand, it is suggested that the operation of a network relying on
harvested energy needs to be redesigned to allow the network to reliably
function in the long term. To this end, the aim of this survey paper is to
provide a comprehensive overview of the recent development and the challenges
regarding the operation of CRNs powered by RF energy. In addition, the
potential open issues that might be considered for the future research are also
discussed in this paper.Comment: 8 pages, 2 figures, 1 table, Accepted in IEEE Communications Magazin
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
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