The Asymmetric Hamming Bidistance and Distributions over Binary Asymmetric Channels
This work addresses a specific problem in communication systems modeling, offering a more discriminative analysis for binary codes, but it is incremental as it builds on prior bounds and focuses on specialized code families.
The paper tackles the problem of analyzing decoding error probabilities for binary asymmetric channels by introducing the asymmetric Hamming bidistance (AHB) and its distribution, which captures directional discrepancies between codewords, and derives a new upper bound on average error probability for maximum-likelihood decoding, showing it is incomparable with existing bounds.
The binary asymmetric channel is a model for practical communication systems where the error probabilities for symbol transitions $0\rightarrow 1$ and $1\rightarrow0$ differ substantially. In this paper, we introduce the notion of asymmetric Hamming bidistance (AHB) and its two-dimensional distribution, which separately captures directional discrepancies between codewords. This finer characterization enables a more discriminative analysis of decoding the error probabilities for maximum-likelihood decoding (MLD), particularly when conventional measures, such as weight distributions and existing discrepancy-based bounds, fail to distinguish code performance. Building on this concept, we derive a new upper bound on the average error probability for binary codes under MLD and show that, in general, it is incomparable with the two existing bounds derived by Cotardo and Ravagnani (IEEE Trans. Inf. Theory, 68 (5), 2022). To demonstrate its applicability, we compute the complete AHB distributions for several families of codes, including two-weight and three-weight projective codes (with the zero codeword removed) via strongly regular graphs and 3-class association schemes, as well as nonlinear codes constructed from symmetric balanced incomplete block designs (SBIBDs).