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Worst-case models of erasure and symmetric channels are investigated, in which the number of channel errors occurring in each sliding window of a given length is bounded. Upper and lower bounds on their zero-error capacities are derived, with the lower bounds revealing a connection with the topological entropy of the channel dynamics. Necessary and sufficient conditions for linear state estimation with bounded estimation errors via such channels are then obtained, by extending previous results for non-stochastic memoryless channels to those with finite memory. These estimation conditions involve the topological entropies of the linear system and the channel.
Stochastic stability for centralized time-varying Kalman filtering over a wireles ssensor network with correlated fading channels is studied. On their route to the gateway, sensor packets, possibly aggregated with measurements from several nodes, may
Although state estimation in networked control systems is a fundamental problem, few efforts have been made to study distributed state estimation via multiple access channels (MACs). In this article, we give a characterization of the zero-error capac
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 $M$-state Markov fading channel, where the p
We consider remote state estimation of multiple discrete-time linear time-invariant (LTI) systems over multiple wireless time-varying communication channels. Each system state is measured by a sensor, and the measurements from sensors are sent to a r
A reconfigurable intelligent surface (RIS) can shape the radio propagation by passively changing the directions of impinging electromagnetic waves. The optimal control of the RIS requires perfect channel state information (CSI) of all the links conne