Dirty-paper Coding without Channel Information at the Transmitter and Imperfect Estimation at the Receiver

TL;DR

This paper investigates the capacity limits of a fading Costa's channel with imperfect receiver estimation and no transmitter channel knowledge, deriving optimal dirty-paper coding schemes and analyzing practical training strategies.

Contribution

It derives the optimal DPC scheme under imperfect channel estimation and no transmitter knowledge, providing insights into practical training design for fading channels.

Findings

Optimal DPC scheme for fading Costa's channel with imperfect estimation

Trade-off between training length and interference cancellation performance

Numerical results demonstrating impact of channel estimation on achievable rates

Abstract

In this paper, we examine the effects of imperfect channel estimation at the receiver and no channel knowledge at the transmitter on the capacity of the fading Costa's channel with channel state information non-causally known at the transmitter. We derive the optimal Dirty-paper coding (DPC) scheme and its corresponding achievable rates with the assumption of Gaussian inputs. Our results, for uncorrelated Rayleigh fading, provide intuitive insights on the impact of the channel estimate and the channel characteristics (e.g. SNR, fading process, channel training) on the achievable rates. These are useful in practical scenarios of multiuser wireless communications (e.g. Broadcast Channels) and information embedding applications (e.g. robust watermarking). We also studied optimal training design adapted to each application. We provide numerical results for a single-user fading Costa's channel with maximum-likehood (ML) channel estimation. These illustrate an interesting practical trade-off between the amount of training and its impact to the interference cancellation performance using DPC scheme.