10 research outputs found
Exact Moderate Deviation Asymptotics in Streaming Data Transmission
In this paper, a streaming transmission setup is considered where an encoder
observes a new message in the beginning of each block and a decoder
sequentially decodes each message after a delay of blocks. In this
streaming setup, the fundamental interplay between the coding rate, the error
probability, and the blocklength in the moderate deviations regime is studied.
For output symmetric channels, the moderate deviations constant is shown to
improve over the block coding or non-streaming setup by exactly a factor of
for a certain range of moderate deviations scalings. For the converse proof, a
more powerful decoder to which some extra information is fedforward is assumed.
The error probability is bounded first for an auxiliary channel and this result
is translated back to the original channel by using a newly developed
change-of-measure lemma, where the speed of decay of the remainder term in the
exponent is carefully characterized. For the achievability proof, a known
coding technique that involves a joint encoding and decoding of fresh and past
messages is applied with some manipulations in the error analysis.Comment: 23 pages, 1 figure, 1 table, Submitted to IEEE Transactions on
Information Theor
Lossless coding for distributed streaming sources
Distributed source coding is traditionally viewed in the block coding context — all the source symbols are known in advance at the encoders. This paper instead considers a streaming setting in which iid source symbol pairs are revealed to the separate encoders in real time and need to be reconstructed at the decoder with some tolerable end-to-end delay using finite rate noiseless channels. A sequential random binning argument is used to derive a lower bound on the error exponent with delay and show that both ML decoding and universal decoding achieve the same positive error exponents inside the traditional Slepian-Wolf rate region. The error events are different from the block-coding error events and give rise to slightly different exponents. Because the sequential random binning scheme is also universal over delays, the resulting code eventually reconstructs every source symbol correctly with probability 1.