Stabilization of Linear Systems Over Gaussian Networks

The problem of remotely stabilizing a noisy linear time invariant plant over a Gaussian relay network is addressed. The network is comprised of a sensor node, a group of relay nodes and a remote controller. The sensor and the relay nodes operate subject to an average transmit power constraint and they can cooperate to communicate the observations of the plant's state to the remote controller. The communication links between all nodes are modeled as Gaussian channels. Necessary as well as sufficient conditions for mean-square stabilization over various network topologies are derived. The sufficient conditions are in general obtained using delay-free linear policies and the necessary conditions are obtained using information theoretic tools. Different settings where linear policies are optimal, asymptotically optimal (in certain parameters of the system) and suboptimal have been identified. For the case with noisy multi-dimensional sources controlled over scalar channels, it is shown that linear time varying policies lead to minimum capacity requirements, meeting the fundamental lower bound. For the case with noiseless sources and parallel channels, non-linear policies which meet the lower bound have been identified

On linear encoder-decoder design for multi-sensor state estimation subject to quantization noise and channel erasure

We consider remote state estimation of a scalar stationary linear Gauss-Markov process observed via noisy measurements obtained by two sensors. The sensors can construct a causal linear function of their measurements, which are quantized and transmitted to a decoder (or fusion centre (FC)) over channels which are prone to packet erasures. We design linear encoding and decoding strategies for estimating the state of the linear system that allow improved estimation performance in the presence of packet erasures and quantization errors. To this end, we construct and compare various distributed encoding and decoding methods without any feedback from the FC regarding the channel erasures. We also design various decentralized benchmark methods that either assume perfect feedback from the FC or in addition co-location of the two sensors resulting in a centralized scheme with diversity. These benchmark methods provide various lower bounds for the distributed encoding-decoding schemes designed without feedback. Numerical results indicate i) that optimal decentralized design of the encoders and the decoder in the absence of feedback can provide a remote state estimation performance that is comparable to those achieved by the lower bounds (with feedback) particularly when the sensors are identical and their channels are symmetric, and (ii) a little feedback from the decoder can improve the performance considerably when the channels are asymmetric (i.e. the packet erasure probabilities are unequal)