TY - GEN
T1 - The Capacity of Causal Adversarial Channels
AU - Zhang, Yihan
AU - Jaggi, Sidharth
AU - Langberg, Michael
AU - Sarwate, Anand D.
N1 - Funding Information:
The work of ADS and ML was supported in part by the US National Science Foundation under awards CCF-1909468 and CCF-1909451.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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 and models.
AB - 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 and models.
KW - arbitrarily varying channels
KW - channel capacity
KW - jamming
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U2 - 10.1109/ISIT50566.2022.9834709
DO - 10.1109/ISIT50566.2022.9834709
M3 - Conference contribution
AN - SCOPUS:85136318745
T3 - IEEE International Symposium on Information Theory - Proceedings
SP - 2523
EP - 2528
BT - 2022 IEEE International Symposium on Information Theory, ISIT 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE International Symposium on Information Theory, ISIT 2022
Y2 - 26 June 2022 through 1 July 2022
ER -