A New Random Coding Technique that Generalizes Superposition Coding and Binning

TL;DR

This paper introduces a novel random coding technique that unifies superposition coding and binning, offering new insights into their relationship and deriving achievable regions for key network models.

Contribution

It proposes a new coding method that generalizes existing techniques and applies it to classical models, enhancing theoretical understanding.

Findings

New achievable regions for multi-access, broadcast, and interference channels.

The new technique unifies superposition coding and binning.

No improvement over existing regions for the studied models.

Abstract

Proving capacity for networks without feedback or cooperation usually involves two fundamental random coding techniques: superposition coding and binning. Although conceptually very different, these two techniques often achieve the same performance, suggesting an underlying similarity. In this correspondence we propose a new random coding technique that generalizes superposition coding and binning and provides new insight on relationship among the two With this new theoretical tool, we derive new achievable regions for three classical information theoretical models: multi-access channel, broadcast channel, the interference channel, and show that, unfortunately, it does not improve over the largest known achievable regions for these cases.