A lower bound on the data rate of dirty paper coding in general noise and interference

TL;DR

This paper derives lower bounds on the data rate of dirty paper coding in scenarios with arbitrary, possibly dependent noise and interference, extending previous Gaussian and independence assumptions.

Contribution

It introduces lower bounds for DPC data rates under general noise and interference distributions, relaxing Gaussian and independence constraints.

Findings

Bounds are simple and based on second-order statistics.

Bounds are tight when noise distribution is near Gaussian.

Extends DPC applicability to more general noise and interference models.

Abstract

Dirty paper coding (DPC) allows a transmitter to send information to a receiver in the presence of interference that is known (non-causally) to the transmitter. The original version of DPC was derived for the case where the noise and the interference are statistically independent Gaussian random sequences. More recent works extended this approach to the case where the noise and the interference are mutually independent and at least one of them is Gaussian. In this letter we further extend the DPC scheme by relaxing the Gaussian and statistical independence assumptions. We provide lower bounds on the achievable data rates in a DPC setting for the case of possibly dependent noise, interference and input signals. Also, the interference and noise terms are allowed to have arbitrary probability distributions. The bounds are relatively simple, are phrased in terms of second-order statistics, and are tight when the actual noise distribution is close to Gaussian.