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Published February 11, 2022 | Accepted Version
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Reliability function for streaming over a DMC with feedback

Abstract

Conventionally, posterior matching is investigated in channel coding and block encoding contexts -- the source symbols are equiprobably distributed and are entirely known by the encoder before the transmission. In this paper, we consider a streaming source, whose symbols arrive at the encoder at a sequence of deterministic times. We derive the joint source-channel coding (JSCC) reliability function for streaming over a discrete memoryless channel (DMC) with feedback. We propose a novel instantaneous encoding phase that operates during the symbol arriving period and achieves the JSCC reliability function for streaming when followed by a block encoding scheme that achieves the JSCC reliability function for a classical source whose symbols are fully accessible before the transmission. During the instantaneous encoding phase, the evolving message alphabet is partitioned into groups, and the encoder determines the index of the group that contains the symbols arrived so far and applies randomization to match the distribution of the transmitted index to the capacity-achieving one. Surprisingly, the JSCC reliability function for streaming is equal to that for a fully accessible source, implying that the knowledge of the entire symbol sequence before the transmission offers no advantage regarding the reliability function. For streaming over a symmetric 2-input DMC, we propose an instantaneous small-enough difference (SED) code that not only achieves the JSCC reliability function but also can be used to stabilize an unstable linear system over a noisy channel. We design low complexity algorithms to implement both the instantaneous encoding phase and the instantaneous SED code. While the reliability function is derived for non-degenerate DMCs, for degenerate DMCs we design a code with instantaneous encoding that achieves zero error for all rates below Shannon's JSCC limit.

Additional Information

This work was supported in part by the National Science Foundation (NSF) under grants CCF-1751356 and CCF-1956386. Insightful comments from Dr. Oron Sabag are gratefully acknowledged.

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Additional details

Created:
August 20, 2023
Modified:
October 23, 2023