4 research outputs found
The benefit of a 1-bit jump-start, and the necessity of stochastic encoding, in jamming channels
We consider the problem of communicating a message in the presence of a
malicious jamming adversary (Calvin), who can erase an arbitrary set of up to
bits, out of transmitted bits . The capacity of such
a channel when Calvin is exactly causal, i.e. Calvin's decision of whether or
not to erase bit depends on his observations was
recently characterized to be . In this work we show two (perhaps)
surprising phenomena. Firstly, we demonstrate via a novel code construction
that if Calvin is delayed by even a single bit, i.e. Calvin's decision of
whether or not to erase bit depends only on (and
is independent of the "current bit" ) then the capacity increases to
when the encoder is allowed to be stochastic. Secondly, we show via a novel
jamming strategy for Calvin that, in the single-bit-delay setting, if the
encoding is deterministic (i.e. the transmitted codeword is a deterministic
function of the message ) then no rate asymptotically larger than is
possible with vanishing probability of error, hence stochastic encoding (using
private randomness at the encoder) is essential to achieve the capacity of
against a one-bit-delayed Calvin.Comment: 21 pages, 4 figures, extended draft of submission to ISIT 201
Upper Bounds on the Capacity of Binary Channels with Causal Adversaries
In this work we consider the communication of information in the presence of
a causal adversarial jammer. In the setting under study, a sender wishes to
communicate a message to a receiver by transmitting a codeword
bit-by-bit over a communication channel. The sender and the receiver do not
share common randomness. The adversarial jammer can view the transmitted bits
one at a time, and can change up to a -fraction of them. However, the
decisions of the jammer must be made in a causal manner. Namely, for each bit
the jammer's decision on whether to corrupt it or not must depend only on
for . This is in contrast to the "classical" adversarial
jamming situations in which the jammer has no knowledge of , or
knows completely. In this work, we present upper bounds (that
hold under both the average and maximal probability of error criteria) on the
capacity which hold for both deterministic and stochastic encoding schemes.Comment: To appear in the IEEE Transactions on Information Theory; shortened
version appeared at ISIT 201
Zero-rate feedback can achieve the empirical capacity
The utility of limited feedback for coding over an individual sequence of
DMCs is investigated. This study complements recent results showing how limited
or noisy feedback can boost the reliability of communication. A strategy with
fixed input distribution is given that asymptotically achieves rates
arbitrarily close to the mutual information induced by and the
state-averaged channel. When the capacity achieving input distribution is the
same over all channel states, this achieves rates at least as large as the
capacity of the state averaged channel, sometimes called the empirical
capacity.Comment: Revised version of paper originally submitted to IEEE Transactions on
Information Theory, Nov. 2007. This version contains further revisions and
clarification
Recommended from our members
Rateless Codes for AVC Models
The arbitrarily varying channel (AVC) is a channel model whose state is selected maliciously by an adversary. Fixed-blocklength coding assumes a worst-case bound on the adversary's capabilities, which leads to pessimistic results. This paper defines a variable-length perspective on this problem, for which achievable rates are shown that depend on the realized actions of the adversary. Specifically, rateless codes are constructed which require a limited amount of common randomness. These codes are constructed for two kinds of AVC models. In the first the channel state cannot depend on the channel input, and in the second it can. As a by-product, the randomized coding capacity of the AVC with state depending on the transmitted codeword is found and shown to be achievable with a small amount of common randomness. The results for this model are proved using a randomized strategy based on list decoding