Stochastic-Adversarial Channels: Online Adversaries With Feedback Snooping

Suresh, Vinayak; Ruzomberka, Eric; Love, David J.

doi:10.1109/isit45174.2021.9517968

Cited by 4 publications

(1 citation statement)

References 16 publications

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“…Capacity results for special cases of causal adversaries with "large alphabets" [DJL13], the erasure setting [BS14], [CJL15], [CJL19], the bit-flip/symbol-error setting [DJLS13], [CJL19], and the quadratically-constrained scenario [LDJ `18] are known. Other related channel models explored include settings with a memoryless jammer [Maz14], and bit-flip and erasure models in which the channel is not state-deterministic but James can observe the channel output [SRL21].…”

Section: Introductionmentioning

confidence: 99%

The Capacity of Causal Adversarial Channels

Zhang¹,

Jaggi²,

Langberg³

et al. 2022

Preprint

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We characterize the capacity for the discrete-time arbitrarily varying channel with discrete inputs, outputs, and states when (a) the encoder and decoder do not share common randomness, (b) the input and state are subject to cost constraints, (c) the transition matrix of the channel is deterministic given the state, and (d) at each time step the adversary can only observe the current and past channel inputs when choosing the state at that time. The achievable strategy involves stochastic encoding together with list decoding and a disambiguation step. The converse uses a two-phase "babble-and-push" strategy where the adversary chooses the state randomly in the first phase, list decodes the output, and then chooses state inputs to symmetrize the channel in the second phase. These results generalize prior work on specific channels models (additive, erasure) to general discrete alphabets

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