arXiv subjects
Publications and source records attributed to Christoph Bunte.
Two maximization problems of Rényi entropy rate are investigated: the maximization over all stochastic processes whose marginals satisfy a linear constraint, and the Burg-like maximization over all stochastic processes whose autocovariance function begins with some given values. The solutions are related to the solutions to the analogous maximization problems of Shannon entropy rate.
The listsize capacity of a discrete memoryless channel is the largest transmission rate for which the expectation---or, more generally, the $ρ$-th moment---of the number of messages that could have produced the output of the channel approaches one as the blocklength tends to infinity. We show that for channels with feedback this rate is upper-bounded by the maximum of Gallager's $E_0$ function divided by $ρ$, and that equality holds when the zero-error capacity of the channel is positive. To establish this inequality we prove that feedback does not increase the cutoff rate. Relationships to other notions of channel capacity are explored.
A task is randomly drawn from a finite set of tasks and is described using a fixed number of bits. All the tasks that share its description must be performed. Upper and lower bounds on the minimum $ρ$-th moment of the number of performed tasks are derived. The case where a sequence of tasks is produced by a source and $n$ tasks are jointly described using $nR$ bits is considered. If $R$ is larger than the Rényi entropy rate of the source of order $1/(1+ρ)$ (provided it exists), then the $ρ$-th moment of the ratio of performed tasks to $n$ can be driven to one as $n$ tends to infinity. If $R$ is smaller than the Rényi entropy rate, this moment tends to infinity. The results are generalized to account for the presence of side-information. In this more general setting, the key quantity is a conditional version of Rényi entropy that was introduced by Arimoto. For IID sources two additional extensions are solved, one of a rate-distortion flavor and the other where different tasks may have different nonnegative costs. Finally, a divergence that was identified by Sundaresan as a mismatch penalty in the Massey-Arikan guessing problem is shown to play a similar role here.
Ahlswede, Cai, and Zhang proved that, in the noise-free limit, the zero-undetected-error capacity is lower bounded by the Sperner capacity of the channel graph, and they conjectured equality. Here we derive an upper bound that proves the conjecture.
A task is randomly drawn from a finite set of tasks and is described using a fixed number of bits. All the tasks that share its description must be performed. Upper and lower bounds on the minimum $ρ$-th moment of the number of performed tasks are derived. The key is an analog of the Kraft Inequality for partitions of finite sets. When a sequence of tasks is produced by a source of a given Rényi entropy rate of order $1/(1+ρ)$ and $n$ tasks are jointly described using $nR$ bits, it is shown that for $R$ larger than the Rényi entropy rate, the $ρ$-th moment of the ratio of performed tasks to $n$ can be driven to one as $n$ tends to infinity, and that for $R$ less than the Rényi entropy rate it tends to infinity. This generalizes a recent result for IID sources by the same authors. A mismatched version of the direct part is also considered, where the code is designed according to the wrong law. The penalty incurred by the mismatch can be expressed in terms of a divergence measure that was shown by Sundaresan to play a similar role in the Massey-Arikan guessing problem.