15,959 research outputs found
Wireless Power Charging Control in Multiuser Broadband Networks
Recent advances in wireless power transfer (WPT) technology provide a
cost-effective solution to charge wireless devices remotely without disruption
to the use. In this paper, we propose an efficient wireless charging control
method for exploiting the frequency diversity in multiuser broadband wireless
networks, to reduce energy outage and keep the system operating in an efficient
and sustainable state. In particular, we first analyze the impact of charging
control method to the operating lifetime of a WPT-enabled broadband system.
Based on the analysis, we then propose a multi-criteria charging control policy
that optimizes the transmit power allocation over frequency by jointly
considering the channel state information (CSI) and the battery state
information (BSI) of wireless devices. For practical implementation, the
proposed scheme is realized by a novel limited CSI estimation mechanism
embedded with partial BSI, which significantly reduces the energy cost of CSI
and BSI feedback. Simulation results show that the proposed method could
significantly increase the network lifetime under stringent transmit power
constraint. Reciprocally, it also consumes lower transmit power to achieve
near-perpetual network operation than other single-criterion based charging
control methods.Comment: This paper had been accepted by IEEE ICC 2015, Workshop on Green
Communications and Networks with Energy Harvesting, Smart Grids, and
Renewable Energie
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
Stochastic Differential Games and Energy-Efficient Power Control
One of the contributions of this work is to formulate the problem of
energy-efficient power control in multiple access channels (namely, channels
which comprise several transmitters and one receiver) as a stochastic
differential game. The players are the transmitters who adapt their power level
to the quality of their time-varying link with the receiver, their battery
level, and the strategy updates of the others. The proposed model not only
allows one to take into account long-term strategic interactions but also
long-term energy constraints. A simple sufficient condition for the existence
of a Nash equilibrium in this game is provided and shown to be verified in a
typical scenario. As the uniqueness and determination of equilibria are
difficult issues in general, especially when the number of players goes large,
we move to two special cases: the single player case which gives us some useful
insights of practical interest and allows one to make connections with the case
of large number of players. The latter case is treated with a mean-field game
approach for which reasonable sufficient conditions for convergence and
uniqueness are provided. Remarkably, this recent approach for large system
analysis shows how scalability can be dealt with in large games and only relies
on the individual state information assumption.Comment: The final publication is available at
http://www.springerlink.com/openurl.asp?genre=article\&id=doi:10.1007/s13235-012-0068-
Distributed stochastic optimization via matrix exponential learning
In this paper, we investigate a distributed learning scheme for a broad class
of stochastic optimization problems and games that arise in signal processing
and wireless communications. The proposed algorithm relies on the method of
matrix exponential learning (MXL) and only requires locally computable gradient
observations that are possibly imperfect and/or obsolete. To analyze it, we
introduce the notion of a stable Nash equilibrium and we show that the
algorithm is globally convergent to such equilibria - or locally convergent
when an equilibrium is only locally stable. We also derive an explicit linear
bound for the algorithm's convergence speed, which remains valid under
measurement errors and uncertainty of arbitrarily high variance. To validate
our theoretical analysis, we test the algorithm in realistic
multi-carrier/multiple-antenna wireless scenarios where several users seek to
maximize their energy efficiency. Our results show that learning allows users
to attain a net increase between 100% and 500% in energy efficiency, even under
very high uncertainty.Comment: 31 pages, 3 figure
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