Multi-marginal couplings combined with an adaptive shared-randomness Poisson Monte Carlo method improve coalescence rates for multiple Metropolis-Hastings chains, cutting meeting times by up to 50%.
One-Shot Broadcast Joint Source-Channel Coding with Codebook Diversity
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abstract
We study a one-shot joint source-channel coding setting where the source is encoded once and broadcast to $K$ decoders through independent channels. Success is predicated on at least one decoder recovering the source within a maximum distortion constraint. We find that in the one-shot regime, utilizing disjoint codebooks at each decoder yields a codebook diversity gain, distinct from the channel diversity gain that may be expected when several decoders observe independent realizations of the channel's output but share the same codebook. Coding schemes are introduced that leverage this phenomenon, where first- and second-order achievability bounds are derived via an adaptation of the Poisson matching lemma which allows for multiple decoders using disjoint codebooks. We further propose a hybrid coding scheme that partitions decoders into groups to optimally balance codebook and channel diversity. Numerical results on the binary symmetric channel demonstrate that the hybrid approach outperforms strategies where the decoders' codebooks are either fully shared or disjoint.
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stat.CO 1years
2026 1verdicts
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Multi-Marginal Couplings for Metropolis-Hastings
Multi-marginal couplings combined with an adaptive shared-randomness Poisson Monte Carlo method improve coalescence rates for multiple Metropolis-Hastings chains, cutting meeting times by up to 50%.