Scaling Laws for Disturbance Propagation in Cyclic Dynamical Networks

Our goal is to analyze performance of stable linear dynamical networks subject to external stochastic disturbances. The square of the ${\mathcal{H}}_2$-norm of the network is used as a performance measure to quantify the expected steady-state dispersion of the outputs of the network. We show that this performance measure can be tightly bounded from below and above by some spectral functions of the state-space matrices of the network. This result is applied to a class of cyclic linear networks and shown that their performance measure scale quadratically with the network size.