AI & ML
Rates and Nearly-Lossless State over a Multi-Access Channel with Causal State Information
arXiv math.IT0 views
arXiv:2610.41860v1 Announce Type: new Abstract: The problem of transmitting both data and state over a memoryless state-dependent multi-access channel with causal state information is addressed. The state comprises three independent components: a common component, which is revealed to both en- coders, and two private components, each of which is revealed to a different encoder. The data are to be recovered with arbitrarily-small probability of error, and the state sequence with arbitrarily-small symbol-error rate. Under the assumption that the capacity region in the absence of a state recon- struction constraint is nonempty, the capacity region is derived and is shown to be achievable using a Block-Markov coding scheme employing binning and superposition coding.
Source：https://arxiv.org/abs/2610.41860PreviousBatch Universal Prediction via Conditional Additive SmoothingNextCapacity Meets the Four Subspace Problem: Distributed Linear Computation and the Cross-Ratio
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