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We investigate the stabilization of unstable multidimensional partiallyobserved single-sensor and multi-sensor linear systems driven by unboundednoise and controlled over discrete noiseless channels under fixed-rateinformation constraints. Stability is achieved under fixed-rate communicationrequirements that are asymptotically tight in the limit of large samplingperiods. Through the use of similarity transforms, sampling and random-timedrift conditions we obtain a coding and control policy leading to the existenceof a unique invariant distribution and finite second moment for the sampledstate. We use a vector stabilization scheme in which all modes of the linearsystem visit a compact set together infinitely often. We prove tight necessaryand sufficient conditions for the general multi-sensor case under an assumptionrelated to the Jordan form structure of such systems. In the absence of thisassumption, we give sufficient conditions for stabilization.

Stochastic Stabilization of Partially Observed and Multi-Sensor Systems Driven by Gaussian Noise under Fixed-Rate Information Constraints