Sample, Quantize and Encode: Timely Estimation Over Noisy Channels

TL;DR

This paper investigates optimal sampling, quantization, and encoding strategies for estimating Gauss-Markov processes over noisy channels, focusing on minimizing mean square error by balancing AoI, quantization, and coding schemes.

Contribution

It introduces optimal sampling policies for IIR and FR coding schemes and analyzes the impact of processing times and quantization on estimation quality.

Findings

Optimal threshold-based sampling for IIR scheme.

Just-in-time sampling policy for fixed redundancy scheme.

Existence of an optimal number of quantization bits balancing AoI and quantization errors.

Abstract

The effects of quantization and coding on the estimation quality of Gauss-Markov processes are considered, with a special attention to the Ornstein-Uhlenbeck process. Samples are acquired from the process, quantized, and then encoded for transmission using either infinite incremental redundancy (IIR) or fixed redundancy (FR) coding schemes. A fixed processing time is consumed at the receiver for decoding and sending feedback to the transmitter. Decoded messages are used to construct a minimum mean square error (MMSE) estimate of the process as a function of time. This is shown to be an increasing functional of the age-of-information (AoI), defined as the time elapsed since the sampling time pertaining to the latest successfully decoded message. Such functional depends on the quantization bits, codewords lengths and receiver processing time. The goal, for each coding scheme, is to optimize sampling times such that the long-term average MMSE is minimized. This is then characterized in the setting of general increasing functionals of AoI, not necessarily corresponding to MMSE, which may be of independent interest in other contexts. We first show that the optimal sampling policy for IIR is such that a new sample is generated only if the AoI exceeds a certain threshold, while for FR it is such that a new sample is delivered just-in-time as the receiver finishes processing the previous one. Enhanced transmissions schemes are then developed in order to exploit the processing times to make new data available at the receiver sooner. For both IIR and FR, it is shown that there exists an optimal number of quantization bits that balances AoI and quantization errors, and hence minimizes the MMSE. It is also shown that for longer receiver processing times, the relatively simpler FR scheme outperforms IIR.