3,248 research outputs found
Optimal Energy Allocation for Kalman Filtering over Packet Dropping Links with Imperfect Acknowledgments and Energy Harvesting Constraints
This paper presents a design methodology for optimal transmission energy
allocation at a sensor equipped with energy harvesting technology for remote
state estimation of linear stochastic dynamical systems. In this framework, the
sensor measurements as noisy versions of the system states are sent to the
receiver over a packet dropping communication channel. The packet dropout
probabilities of the channel depend on both the sensor's transmission energies
and time varying wireless fading channel gains. The sensor has access to an
energy harvesting source which is an everlasting but unreliable energy source
compared to conventional batteries with fixed energy storages. The receiver
performs optimal state estimation with random packet dropouts to minimize the
estimation error covariances based on received measurements. The receiver also
sends packet receipt acknowledgments to the sensor via an erroneous feedback
communication channel which is itself packet dropping.
The objective is to design optimal transmission energy allocation at the
energy harvesting sensor to minimize either a finite-time horizon sum or a long
term average (infinite-time horizon) of the trace of the expected estimation
error covariance of the receiver's Kalman filter. These problems are formulated
as Markov decision processes with imperfect state information. The optimal
transmission energy allocation policies are obtained by the use of dynamic
programming techniques. Using the concept of submodularity, the structure of
the optimal transmission energy policies are studied. Suboptimal solutions are
also discussed which are far less computationally intensive than optimal
solutions. Numerical simulation results are presented illustrating the
performance of the energy allocation algorithms.Comment: Submitted to IEEE Transactions on Automatic Control. arXiv admin
note: text overlap with arXiv:1402.663
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-
Partner selection in indoor-to-outdoor cooperative networks: an experimental study
In this paper, we develop a partner selection protocol for enhancing the
network lifetime in cooperative wireless networks. The case-study is the
cooperative relayed transmission from fixed indoor nodes to a common outdoor
access point. A stochastic bivariate model for the spatial distribution of the
fading parameters that govern the link performance, namely the Rician K-factor
and the path-loss, is proposed and validated by means of real channel
measurements. The partner selection protocol is based on the real-time
estimation of a function of these fading parameters, i.e., the coding gain. To
reduce the complexity of the link quality assessment, a Bayesian approach is
proposed that uses the site-specific bivariate model as a-priori information
for the coding gain estimation. This link quality estimator allows network
lifetime gains almost as if all K-factor values were known. Furthermore, it
suits IEEE 802.15.4 compliant networks as it efficiently exploits the
information acquired from the receiver signal strength indicator. Extensive
numerical results highlight the trade-off between complexity, robustness to
model mismatches and network lifetime performance. We show for instance that
infrequent updates of the site-specific model through K-factor estimation over
a subset of links are sufficient to at least double the network lifetime with
respect to existing algorithms based on path loss information only.Comment: This work has been submitted to IEEE Journal on Selected Areas in
Communications in August 201
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