4,815 research outputs found
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
Energy Harvesting Broadband Communication Systems with Processing Energy Cost
Communication over a broadband fading channel powered by an energy harvesting
transmitter is studied. Assuming non-causal knowledge of energy/data arrivals
and channel gains, optimal transmission schemes are identified by taking into
account the energy cost of the processing circuitry as well as the transmission
energy. A constant processing cost for each active sub-channel is assumed.
Three different system objectives are considered: i) throughput maximization,
in which the total amount of transmitted data by a deadline is maximized for a
backlogged transmitter with a finite capacity battery; ii) energy maximization,
in which the remaining energy in an infinite capacity battery by a deadline is
maximized such that all the arriving data packets are delivered; iii)
transmission completion time minimization, in which the delivery time of all
the arriving data packets is minimized assuming infinite size battery. For each
objective, a convex optimization problem is formulated, the properties of the
optimal transmission policies are identified, and an algorithm which computes
an optimal transmission policy is proposed. Finally, based on the insights
gained from the offline optimizations, low-complexity online algorithms
performing close to the optimal dynamic programming solution for the throughput
and energy maximization problems are developed under the assumption that the
energy/data arrivals and channel states are known causally at the transmitter.Comment: published in IEEE Transactions on Wireless Communication
Communicating Using an Energy Harvesting Transmitter: Optimum Policies Under Energy Storage Losses
In this paper, short-term throughput optimal power allocation policies are
derived for an energy harvesting transmitter with energy storage losses. In
particular, the energy harvesting transmitter is equipped with a battery that
loses a fraction of its stored energy. Both single user, i.e. one
transmitter-one receiver, and the broadcast channel, i.e., one
transmitter-multiple receiver settings are considered, initially with an
infinite capacity battery. It is shown that the optimal policies for these
models are threshold policies. Specifically, storing energy when harvested
power is above an upper threshold, retrieving energy when harvested power is
below a lower threshold, and transmitting with the harvested energy in between
is shown to maximize the weighted sum-rate. It is observed that the two
thresholds are related through the storage efficiency of the battery, and are
nondecreasing during the transmission. The results are then extended to the
case with finite battery capacity, where it is shown that a similar
double-threshold structure arises but the thresholds are no longer monotonic. A
dynamic program that yields an optimal online power allocation is derived, and
is shown to have a similar double-threshold structure. A simpler online policy
is proposed and observed to perform close to the optimal policy.Comment: Submitted to IEEE Transactions on Wireless Communications, August
201
Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey
Wireless sensor networks (WSNs) consist of autonomous and resource-limited
devices. The devices cooperate to monitor one or more physical phenomena within
an area of interest. WSNs operate as stochastic systems because of randomness
in the monitored environments. For long service time and low maintenance cost,
WSNs require adaptive and robust methods to address data exchange, topology
formulation, resource and power optimization, sensing coverage and object
detection, and security challenges. In these problems, sensor nodes are to make
optimized decisions from a set of accessible strategies to achieve design
goals. This survey reviews numerous applications of the Markov decision process
(MDP) framework, a powerful decision-making tool to develop adaptive algorithms
and protocols for WSNs. Furthermore, various solution methods are discussed and
compared to serve as a guide for using MDPs in WSNs
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