Practical Short-Length Coding Schemes for Binary Distributed Hypothesis Testing
🤖 AI Summary
This work addresses the binary distributed hypothesis testing (DHT) problem. We propose, for the first time, practical short-length binary linear block code constructions—including quantize-and-encode and quantize-and-bin schemes—tailored to finite-blocklength DHT. Theoretically, we establish a rigorous closed-form analytical framework, deriving exact analytical expressions for both Type-I and Type-II error probabilities; this bridges the gap between asymptotic information-theoretic bounds and finite-length operational performance. Experimental validation confirms the high accuracy of our formulas and demonstrates their utility in quantitatively comparing the Type-II error exponents of diverse coding schemes. Collectively, our approach provides a new paradigm for designing and evaluating short-length DHT systems—one that is analytically tractable, quantitatively comparable, and practically implementable.
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📝 Abstract
This paper investigates practical coding schemes for Distributed Hypothesis Testing (DHT). While the literature has extensively analyzed the information-theoretic performance of DHT and established bounds on Type-II error exponents through quantize and quantize-binning achievability schemes, the practical implementation of DHT coding schemes has not yet been investigated. Therefore, this paper introduces practical implementations of quantizers and quantize-binning schemes for DHT, leveraging short-length binary linear block codes. Furthermore, it provides exact analytical expressions for Type-I and Type-II error probabilities associated with each proposed coding scheme. Numerical results show the accuracy of the proposed analytical error probability expressions, and enable to compare the performance of the proposed schemes.
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