Shaped Gaussian Dictionaries for Quantized Networked Control Systems with Correlated Dropouts

TL;DR

This paper introduces a shaped Gaussian dictionary design for quantized control over noisy network channels with correlated dropouts, improving efficiency by leveraging system statistics without training.

Contribution

It proposes a novel shaped Gaussian dictionary approach for vector quantization in networked control systems, utilizing second-order statistics for improved performance.

Findings

Significant gain using shaped Gaussian dictionaries over i.i.d. ones.

Closed-form expressions for second-order statistics without training.

Enhanced control performance over error-prone channels.

Abstract

This paper studies fixed rate vector quantisation for noisy networked control systems (NCSs) with correlated packet dropouts. In particular, a discrete-time linear time invariant system is to be controlled over an error-prone digital channel. The controller uses (quantized) packetized predictive control to reduce the impact of packet losses. The proposed vector quantizer is based on sparse regression codes (SPARC), which have recently been shown to be efficient in open-loop systems when coding white Gaussian sources. The dictionaries in existing design of SPARCs consist of independent and identically distributed (i.i.d.) Gaussian entries. However, we show that a significant gain can be achieved by using Gaussian dictionaries that are shaped according to the second-order statistics of the NCS in question. Furthermore, to avoid training of the dictionaries, we provide closed-form expressions for the required second-order statistics in the absence of quantization.