AI & ML
Identification for Inverse Gaussian Channels
arXiv math.IT0 views
arXiv:2605.06103v3 Announce Type: replace Abstract: We derive lower and upper bounds on the identification capacity of inverse Gaussian (IG) channels, a fundamental model for molecular communications in fluid environments. The analysis considers deterministic encoding schemes under a peak time constraint and characterizes the asymptotic growth of optimal codebook sizes. In the converse, by leveraging the Borel-Cantelli lemma and the super-exponential near-zero tail behavior of the IG distribution, we establish an almost-sure lower bound on the first-passage time of drifting diffusing molecules. This leads to the conclusion that the identification capacity exhibits super-exponential growth with the codeword length $n$, i.e., $\sim 2^{(n \log n)R_n}$, where $R_n$ is the coding rate.
Source:https://arxiv.org/abs/2605.06103PreviousPhased Ultra Massive Array (PUMA)NextPolar Complexity: A New Descriptive Complexity with Applications to Source and Joint Source-Channel Coding
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