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IEEE International Symposium on Information Theory (ISIT)

The AVC with noiseless feedback and maximal error probability: a capacity formula with a trichotomy

Rudolf Ahlswede, Ning Cai

Published Jan 1, 1997• 2 citations
Publisher

Abstract

Summary form only given. To use common randomness in coding is a key idea from the theory of identification. Methods and ideas of this theory are shown here to have also an impact on Shannon's theory of transmission. We determine the capacity for a classical arbitrarily varying channel (AVC) (Ahlswede, 1973) with a novel structure of the capacity formula. This channel models a robust search problem in the presence of noise (Ahlswede and Wegner 1987).

DOI
10.1109/isit.1997.613047

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•Jan 1, 2021•24 citations
Summary form only given. To use common randomness in coding is a key idea from the theory of identification. Methods and ideas of this theory are shown here to have also an impact on Shannon's theory of transmission. We determine the capacity for a classical arbitrarily varying channel (AVC) (Ahlswede, 1973) with a novel structure of the capacity formula. This channel models a robust search problem in the presence of noise (Ahlswede and Wegner 1987).
In the theory of identification via noisy channels randomization in the encoding has a dramatic effect on the optimal code size, namely, it grows double-exponentially in the blocklength, whereas in the theory of transmission it has the familiar exponential growth. We consider now instead of the discrete memoryless channel (DMC) more robust channels such as the familiar compound (CC) and arbitrarily varying channels (AVC). They can be viewed as models for jamming situations. We make the pessimistic assumption that the jammer knows the input sequence before he acts. This forces communicators to use the maximal error concept and also makes randomization in the encoding superfluous. Now, for a DMC W by a simple observation, made by Ahlswede and Dueck (1989), in the absence of randomization the identification capacity, say C/sub NRI/(W), equals the logarithm of the number of different row-vectors in W. We generalize this to compound channels. A formidable problem arises if the DMC W is replaced by the AVC W. In fact, for 0-1-matrices only in W we are-exactly as for transmission-led to the equivalent zero-error-capacity of Shannon. But for general W the identification capacity C/sub NRI/(W) is quite different from the transmission capacity C(W). An observation is that the separation codes of Ahlswede (1989) are also relevant here. We present a lower bound on C/sub NRI/(W). It implies for instance for W={(/sub 0 1//sup 1 0/), (/sub /spl delta/ (1-/spl delta/)(1 0)/)}, /spl delta//spl isin/(0, 1/2 ) that C/sub NRI/(W)=1, which is obviously tight. It exceeds C(W), which is known to exceed 1-h(/spl delta/), where h is the binary entropy function. We observe that a separation code, with worst case average list size L~ (which we call an NRA code) can be partitioned into L~2/sup ne/ transmission codes. This gives a nonsingle-letter characterization of the capacity of AVC with maximal probability of error in terms of the capacity of codes with list decoding. We also prove that randomization in the decoding does not increase C/sub I/(W) and C/sub NRI/(W). Finally, we draw attention to related work on source coding.
A study is made of the identification problem in the presence of a noiseless feedback channel, and the second-order capacity C/sub f/ (resp. C/sub F/) for deterministic (resp. randomized) encoding strategies is determined. Several important phenomena are encountered. (1) Although feedback does not increase the transmission capacity of a discrete memoryless channel (DMC), it does increase the (second-order) identification capacity; (2) noise increases C/sub f/; (3) the structure of the new capacity formulas is simpler than C.E. Shannon's (1948) familiar formula. This has the effect that proofs of converses become easier than in the authors' previous work.>
We analyze message identification via Gaussian channels with noiseless feedback, which is part of the Post Shannon theory. The consideration of communication systems beyond Shannon's approach is useful in order to increase the efficiency of information transmission for certain applications. If the noise variance is positive, we propose a coding scheme that generates infinite common randomness between the sender and the receiver. We show that any identification rate via the Gaussian channel with noiseless feedback can be achieved. The remarkable result is that this applies to both rate definitions1nlog⁡M\frac{1}{n}\log Mn1​logM(as defined by Shannon for transmission) and1n log⁡log⁡ M\frac{1}{n}\ \log \log\ Mn1​ loglog M— (as defined by Ahlswede and Dueck for identification). We can even show that our result holds regardless of the selected scaling for the rate. A detailed version with all proofs, explanations and more discussions can be found in [1].
The arbitrarily varying channel can be modeled as communication in the presence of a jammer. In this paper we propose a new model: a jammer who knows the channel input, and where the transmitter and receiver share a secret random key. Shared randomness differentiates this scenario from the case where the jammer knows the message. For sufficiently large key rate, we determine the capacity of this channel (which may be strictly smaller than the case where the jammer knows only the message). We also provide an upper bound on the minimum key rate required to achieve capacity. We prove that additionally revealing the message to the jammer does not change the capacity, provided the key rate is sufficiently large. This new capacity result differs from existing results for the AVC, and in fact coincides with a well-known upper bound on the deterministic coding capacity of the AVC with maximum error. Without secret keys, our problem degenerates to deterministic coding for the AVC with maximum probability of error, a well-known hard problem. Our results demonstrate that knowledge of the channel input is better than knowledge of the message for the jammer.
We analyze message identification via Gaussian channels with noiseless feedback, which is part of the Post Shannon theory. The consideration of communication systems beyond Shannon's approach is useful in order to increase the efficiency of information transmission for certain applications. If the noise variance is positive, we propose a coding scheme that generates infinite common randomness between the sender and the receiver. We show that any identification rate via the Gaussian channel with noiseless feedback can be achieved. The remarkable result is that this applies to both rate definitions 1nlog⁡M\frac{1}{n}\log Mn1​logM (as defined by Shannon for transmission) and 1n log⁡log⁡ M\frac{1}{n}\ \log \log\ Mn1​ loglog M — (as defined by Ahlswede and Dueck for identification). We can even show that our result holds regardless of the selected scaling for the rate. A detailed version with all proofs, explanations and more discussions can be found in [1].