In this paper we consider the problem of remote state estimation of a Gauss-Markov process, where a sensor can, at each discrete time instant, transmit on one out of M different communication channels. A key difficulty of the situation at hand is that the channel statistics are unknown. We study the case where both learning of the channel reception probabilities and state estimation is carried out simultaneously. Methods for choosing the channels based on techniques for multi-armed bandits are presented, and shown to provide stability. Furthermore, we define the performance notion of estimation regret, and derive bounds on how it scales with time for the considered algorithms.</p
This paper considers optimal attack attention allocation on remote state estimation in multi-systems...
In this paper, we consider the problem of power scheduling of a sensor that transmits over a (possib...
In this paper we consider the problem of designing coding and decoding schemes to estimate the state...
We investigate the stability conditions for remote state estimation of multiple linear time-invarian...
We consider a fundamental remote state estimation problem of discrete-time linear time-invariant (LT...
This letter studies remote state estimation of multiple linear time-invariant systems over shared wi...
Abstract Wireless devices are often able to communicate on several alternative channels; for example...
Remote state estimation problems in the presence of an eavesdropper have recently been studied. In ...
Wireless devices are often able to communicate on several alternative channels; for example, cellula...
We consider scheduling two Gauss-Markov systems. Two sensors, each measuring the state of one of the...
We consider remote state estimation of a scalar stationary linear Gauss-Markov process observed via ...
Abstract — We consider the channel sensing problem arising in opportunistic scheduling over fading c...
We consider the task of optimally sensing a two-state Markovian channel with an observation cost and...
This dissertation investigates communication and estimation over channels whose transmission charact...
This paper considers a remote state estimation problem with multiple sensors observing a dynamical p...
This paper considers optimal attack attention allocation on remote state estimation in multi-systems...
In this paper, we consider the problem of power scheduling of a sensor that transmits over a (possib...
In this paper we consider the problem of designing coding and decoding schemes to estimate the state...
We investigate the stability conditions for remote state estimation of multiple linear time-invarian...
We consider a fundamental remote state estimation problem of discrete-time linear time-invariant (LT...
This letter studies remote state estimation of multiple linear time-invariant systems over shared wi...
Abstract Wireless devices are often able to communicate on several alternative channels; for example...
Remote state estimation problems in the presence of an eavesdropper have recently been studied. In ...
Wireless devices are often able to communicate on several alternative channels; for example, cellula...
We consider scheduling two Gauss-Markov systems. Two sensors, each measuring the state of one of the...
We consider remote state estimation of a scalar stationary linear Gauss-Markov process observed via ...
Abstract — We consider the channel sensing problem arising in opportunistic scheduling over fading c...
We consider the task of optimally sensing a two-state Markovian channel with an observation cost and...
This dissertation investigates communication and estimation over channels whose transmission charact...
This paper considers a remote state estimation problem with multiple sensors observing a dynamical p...
This paper considers optimal attack attention allocation on remote state estimation in multi-systems...
In this paper, we consider the problem of power scheduling of a sensor that transmits over a (possib...
In this paper we consider the problem of designing coding and decoding schemes to estimate the state...