Practical Short-Length Coding Schemes for Binary Distributed Hypothesis Testing

TL;DR

This paper develops practical short-length binary coding schemes for distributed hypothesis testing, providing analytical error probability formulas and demonstrating improved performance over existing methods.

Contribution

It introduces efficient short-length quantization and quantize-binning schemes based on binary linear codes for DHT, with exact error probability analysis.

Findings

Proposed coding schemes outperform uncoded and existing data reconstruction codes.

Derived exact analytical expressions for Type-I and Type-II error probabilities.

Analytical predictions match Monte Carlo simulation results.

Abstract

This paper addresses the design of practical shortlength coding schemes for Distributed Hypothesis Testing (DHT). While most prior work on DHT has focused on informationtheoretic analyses, deriving bounds on Type-II error exponents via achievability schemes based on quantization and quantizebinning, the practical implementation of DHT coding schemes has remained largely unexplored. Moreover, existing practical coding solutions for quantization and quantize-binning approaches were developed for source reconstruction tasks considering very long code length, and they are not directly applicable to DHT. In this context, this paper introduces efficient shortlength implementations of quantization and quantize-binning schemes for DHT, constructed from short binary linear block codes. Numerical results show the efficiency of the proposed coding schemes compared to uncoded cases and to existing schemes initially developed for data reconstruction. In addition to practical code design, the paper derives exact analytical expressions for the Type-I and Type-II error probabilities associated with each proposed scheme. The provided analytical expressions are shown to predict accurately the practical performance measured from Monte-Carlo simulations of the proposed schemes. These theoretical results are novel and offer a useful framework for optimizing and comparing practical DHT schemes across a wide range of source and code parameters.