LAUSR.org creates dashboard-style pages of related content for over 1.5 million academic articles. Sign Up to like articles & get recommendations!

A Unified Random Coding Bound

Photo by lucabravo from unsplash

In this paper, we prove a unified achievability bound that generalizes and improves random coding bounds for any combination of source coding, channel coding, joint source–channel coding, and coding for… Click to show full abstract

In this paper, we prove a unified achievability bound that generalizes and improves random coding bounds for any combination of source coding, channel coding, joint source–channel coding, and coding for computing problems assuming blockwise node operation. As a general network setup, we consider an acyclic discrete memoryless network, where the network demands and constraints are specified by a joint-typicality constraint on the whole channel input and output sequences. For achievability, a basic building block for node operation consists of simultaneous nonunique decoding, simultaneous compression, and symbol-by-symbol mapping. Our bound can be useful for deriving random coding bounds without error analysis, especially for large and complex networks. In particular, our bound can be used for unifying and generalizing many known relaying strategies. For example, a generalized decode-compress-amplify-and-forward bound is obtained as a simple corollary of our main theorem, and it is shown to strictly outperform the previously known relaying schemes. Furthermore, by exploiting the symmetry in our bound, we formally define and characterize three types of network duality based on channel input–output reversal and network flow reversal combined with packing–covering duality.

Keywords: network; random coding; bound; channel; coding bound; unified random

Journal Title: IEEE Transactions on Information Theory
Year Published: 2018

Link to full text (if available)


Share on Social Media:                               Sign Up to like & get
recommendations!

Related content

More Information              News              Social Media              Video              Recommended



                Click one of the above tabs to view related content.