Characterization and Modeling of Real Communication Channels,
Abstract
The paper presents new descriptive and generative models for the error-cluster and error gap patterns which occur in the binary, discrete-time stochastic processes observed as outputs of digital communication channels having memory. The slope of the error-gap distribution is used to uncover relationships between various channel models. One characterizes the memory mu of a process of error density Pe by its relative deviation in average conditional entropy from the discrete memoryless channel (D.M.C.), which one proves has maximum entropy for the class of (finite and infinite memory length) processes of density Pe. One obtains an upper bound for mu for real channels, derive mu for the general discrete renewal process from the error gap probability mass function (EGPMF) and prove that it is a lower bound for any processes having the same EGPMF. One demonstrates some limitations of finite error-free state models by showing that their EGPMF is bounded from above by a geometric series. To estimate the counting distribution with flexibility we introduce conditional gap distributions and multigap statistics; one uses these in implementing a denumerable Markov Chain model which, free from finite state model limitations and more general than renewal processes, allows the derivation of all classical statistics including entropy. (Author)
Document Details
- Document Type
- Technical Report
- Publication Date
- Jul 31, 1970
- Accession Number
- AD0715288
Entities
People
- Bruce D. Fritchman
- Jean-pierre A. Adoul
- Laveen N. Kanal
Organizations
- Lehigh University