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This paper studies a specific sequential decision making problem: joint optimization of power and communication decisions of a remote estimation problem over an additive noise channel. At each time, the sensor observes a sample from a one-dimensional stochastic process, and then decides whether to transmit its observation to the remote estimator or not, which takes place over an additive noise channel...
This paper considers a sequential estimation and sensor scheduling problem with one sensor and one estimator. The sensor makes sequential observations about the state of an underlying memoryless stochastic process, and makes a decision as to whether or not to send this measurement to the estimator. The sensor and the estimator have the common objective of minimizing expected distortion in the estimation...
When is optimal estimation linear? It is well-known that, in the case of a Gaussian source contaminated with Gaussian noise, a linear estimator minimizes the mean square estimation error. This paper analyzes more generally the conditions for linearity of optimal estimators. Given a noise (or source) distribution, and a specified signal to noise ratio (SNR), we derive conditions for existence and uniqueness...
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