Indirect NRDF for Partially Observable Gauss-Markov Processes with MSE
Distortion: Complete Characterizations and Optimal Solutions
Abstract
In this paper we study the problem of characterizing and computing the
nonanticipative rate distortion function (NRDF) for partially observable
multivariate Gauss-Markov processes with hard mean squared error (MSE)
distortion constraints. For the finite time horizon case, we first
derive the complete characterization of this problem and its
corresponding optimal realization which is shown to be a linear
functional of the current time sufficient statistic of the past and
current observations signals. We show that when the problem is strictly
feasible, it can be computed via semidefinite programming (SDP)
algorithm. For time-varying scalar processes with average total MSE
distortion we derive an optimal closed form expression by means of a
dynamic reverse-waterfilling solution that we also implement via an
iterative scheme that convergences linearly in finite time, and a
closed-form solution under pointwise MSE distortion constraint. For the
infinite time horizon, we give necessary and sufficient conditions to
sure that asymptotically the sufficient statistic process of the
observation signals achieves a steady-state solution for the
corresponding covariance matrices and impose conditions that allow
existence of a time-invariant solution. Then, we show that when a finite
solution exists in the asymptotic limit, it can be computed via SDP
algorithm. We also give strong structural properties on the
characterization of the problem in the asymptotic limit that allow for
an optimal solution via a reverse-waterfilling algorithm that we
implement via an iterative scheme that converges linearly under a finite
number of spatial components. Subsequently, we compare the computational
time needed to execute for both SDP and reverse-waterfilling algorithms
when these solve the same problem to show that the latter is a scalable
optimization technique. Our results are corroborated with various
simulation studies and are also compared with existing results in the
literature.