77,649 research outputs found
Deep Reinforcement Learning for Wireless Sensor Scheduling in Cyber-Physical Systems
In many Cyber-Physical Systems, we encounter the problem of remote state
estimation of geographically distributed and remote physical processes. This
paper studies the scheduling of sensor transmissions to estimate the states of
multiple remote, dynamic processes. Information from the different sensors have
to be transmitted to a central gateway over a wireless network for monitoring
purposes, where typically fewer wireless channels are available than there are
processes to be monitored. For effective estimation at the gateway, the sensors
need to be scheduled appropriately, i.e., at each time instant one needs to
decide which sensors have network access and which ones do not. To address this
scheduling problem, we formulate an associated Markov decision process (MDP).
This MDP is then solved using a Deep Q-Network, a recent deep reinforcement
learning algorithm that is at once scalable and model-free. We compare our
scheduling algorithm to popular scheduling algorithms such as round-robin and
reduced-waiting-time, among others. Our algorithm is shown to significantly
outperform these algorithms for many example scenarios
Performance Analysis of a 5G Transceiver Implementation for Remote Areas Scenarios
The fifth generation of mobile communication networks will support a large
set of new services and applications. One important use case is the remote area
coverage for broadband Internet access. This use case ha significant social and
economic impact, since a considerable percentage of the global population
living in low populated area does not have Internet access and the
communication infrastructure in rural areas can be used to improve agribusiness
productivity. The aim of this paper is to analyze the performance of a 5G for
Remote Areas transceiver, implemented on field programmable gate array based
hardware for real-time processing. This transceiver employs the latest digital
communication techniques, such as generalized frequency division multiplexing
waveform combined with 2 by 2 multiple-input multiple-output diversity scheme
and polar channel coding. The performance of the prototype is evaluated
regarding its out-of-band emissions and bit error rate under AWGN channel.Comment: Presented in 2018 European Conference on Networks and Communications
(EuCNC),18-21 June, 2018, Ljubljana, Sloveni
Remote State Estimation with Smart Sensors over Markov Fading Channels
We consider a fundamental remote state estimation problem of discrete-time
linear time-invariant (LTI) systems. A smart sensor forwards its local state
estimate to a remote estimator over a time-correlated -state Markov fading
channel, where the packet drop probability is time-varying and depends on the
current fading channel state. We establish a necessary and sufficient condition
for mean-square stability of the remote estimation error covariance as
, where denotes the
spectral radius, is the state transition matrix of the LTI system,
is a diagonal matrix containing the packet drop probabilities in
different channel states, and is the transition probability matrix
of the Markov channel states. To derive this result, we propose a novel
estimation-cycle based approach, and provide new element-wise bounds of matrix
powers. The stability condition is verified by numerical results, and is shown
more effective than existing sufficient conditions in the literature. We
observe that the stability region in terms of the packet drop probabilities in
different channel states can either be convex or concave depending on the
transition probability matrix . Our numerical results suggest that
the stability conditions for remote estimation may coincide for setups with a
smart sensor and with a conventional one (which sends raw measurements to the
remote estimator), though the smart sensor setup achieves a better estimation
performance.Comment: The paper has been accepted by IEEE Transactions on Automatic
Control. Copyright may be transferred without notice, after which this
version may no longer be accessibl
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