Rényi Bounds on Information Combining
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Bounds on information combining are entropic inequalities that determine how the information, or entropy, of a set of random variables can change when they are combined in certain prescribed ways. Such bounds play an important role in information theory, particularly in coding and Shannon theory. The arguably most elementary kind of information combining is the addition of two binary random variables, i.e. a CNOT gate, and the resulting quantities are fundamental when investigating belief propagation and polar coding.In this work we will generalize the concept to Rényi entropies. We give optimal bounds on the conditional Rényi entropy after combination, based on a certain convexity or concavity property and discuss when this property indeed holds. Since there is no generally agreed upon definition of the conditional Rényi entropy, we consider four different versions from the literature.Finally, we discuss the application of these bounds to the polarization of Rényi entropies under polar codes.
Original language | English |
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Title of host publication | 2020 IEEE International Symposium on Information Theory, ISIT 2020 - Proceedings |
Publisher | IEEE |
Publication date | 2020 |
Pages | 2297-2302 |
Article number | 9174256 |
ISBN (Electronic) | 9781728164328 |
DOIs | |
Publication status | Published - 2020 |
Event | 2020 IEEE International Symposium on Information Theory, ISIT 2020 - Los Angeles, United States Duration: 21 Jul 2020 → 26 Jul 2020 |
Conference
Conference | 2020 IEEE International Symposium on Information Theory, ISIT 2020 |
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Land | United States |
By | Los Angeles |
Periode | 21/07/2020 → 26/07/2020 |
Sponsor | IEEE Information Theory Society, The Institute of Electrical and Electronics Engineers |
ID: 256725137