English

Improving on the Cut-Set Bound via Geometric Analysis of Typical Sets

Information Theory 2016-03-31 v2 math.IT

Abstract

We consider the discrete memoryless symmetric primitive relay channel, where, a source XX wants to send information to a destination YY with the help of a relay ZZ and the relay can communicate to the destination via an error-free digital link of rate R0R_0, while YY and ZZ are conditionally independent and identically distributed given XX. We develop two new upper bounds on the capacity of this channel that are tighter than existing bounds, including the celebrated cut-set bound. Our approach significantly deviates from the standard information-theoretic approach for proving upper bounds on the capacity of multi-user channels. We build on the blowing-up lemma to analyze the probabilistic geometric relations between the typical sets of the nn-letter random variables associated with a reliable code for communicating over this channel. These relations translate to new entropy inequalities between the nn-letter random variables involved. As an application of our bounds, we study an open question posed by (Cover, 1987), namely, what is the minimum needed ZZ-YY link rate R0R_0^* in order for the capacity of the relay channel to be equal to that of the broadcast cut. We consider the special case when the XX-YY and XX-ZZ links are both binary symmetric channels. Our tighter bounds on the capacity of the relay channel immediately translate to tighter lower bounds for R0R_0^*. More interestingly, we show that when p1/2p\to 1/2, R00.1803R_0^*\geq 0.1803; even though the broadcast channel becomes completely noisy as p1/2p\to 1/2 and its capacity, and therefore the capacity of the relay channel, goes to zero, a strictly positive rate R0R_0 is required for the relay channel capacity to be equal to the broadcast bound.

Keywords

Cite

@article{arxiv.1602.08540,
  title  = {Improving on the Cut-Set Bound via Geometric Analysis of Typical Sets},
  author = {Xiugang Wu and Ayfer Ozgur and Liang-Liang Xie},
  journal= {arXiv preprint arXiv:1602.08540},
  year   = {2016}
}