A Lower Bound on the Data Rate of Dirty Paper Coding in General Noise and Interference

Abstract

Dirty paper coding (DPC) allows a transmitter to send information to a receiver in the presence of interference that is known (noncausally) 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. Moreover, 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.