Pith. sign in

REVIEW 1 cited by

Error Exponents of Typical Random Trellis Codes

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1903.01120 v1 pith:YT33MEQN submitted 2019-03-04 cs.IT math.IT

classification cs.ITmath.IT
keywords errorcodesrandomtypicalexponenttrellischannelsderive
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original abstract

In continuation to an earlier work, where error exponents of typical random codes were studied in the context of general block coding, with no underlying structure, here we carry out a parallel study on typical random, time-varying trellis codes for general discrete memoryless channels, focusing on a certain range of low rates. By analyzing an upper bound to the error probability of the typical random trellis code, using the method of types, we first derive a Csiszar-style error exponent formula (with respect to the constraint length), which allows to easily identify and characterize properties of good codes and dominant error events. We also derive a Gallager-style form of this error exponent, which turns out to be related to the expurgated error exponent. The main result is further extended to channels with memory and mismatch.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. A Lagrange-Dual Lower Bound to the Error Exponent Function of the Typical Random Code

    cs.IT 2019-08 conditional novelty 6.0 of 10

    A five-parameter Lagrange-dual lower bound is derived for the typical random code error exponent for i.i.d. ensembles and mismatched likelihood decoding.

Pith tools