| Title | Channel Coding Theorems in Non-stochastic Information Theory |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Rangi, Anshuka, Franceschetti, Massimo |
| Conference Name | 2021 IEEE International Symposium on Information Theory (ISIT) |
| Date Published | jul |
| Keywords | (\$\textbackslashtextbackslashepsilon, \textbackslashtextbackslashdelta\$)-capacity, channel coding, Channel estimation, coding theorem, composability, Computer science, Estimation, Kolmogorov capacity, Learning systems, Metrics, Mutual information, pubcrawl, Resiliency, security, Shannon capacity, sufficient conditions, zero-error capacity |
| Abstract | Recently, the d-mutual information between uncertain variables has been introduced as a generalization of Nair's non-stochastic mutual information functional [1], [2]. Within this framework, we introduce four different notions of capacity and present corresponding coding theorems. Our definitions include an analogue of Shannon's capacity in a non-stochastic setting, and a generalization of the zero-error capacity. The associated coding theorems hold for stationary, memoryless, non-stochastic uncertain channels. These results establish the relationship between the d-mutual information and our operational definitions, providing a step towards the development of a complete non-stochastic information theory. |
| DOI | 10.1109/ISIT45174.2021.9518008 |
| Citation Key | rangi_channel_2021 |
Channel Coding Theorems in Non-stochastic Information Theory