On the Achievable Rate of the Fading Dirty Paper Channel with Imperfect CSIT

TL;DR

This paper analyzes the achievable data rates of the fading dirty paper channel with imperfect channel knowledge at the transmitter, extending previous work to more general input covariance matrices and deriving new high-SNR scaling laws.

Contribution

It generalizes the high-SNR scaling factor for the fading dirty paper channel to positive semi-definite input covariance matrices and develops algorithms for joint optimization of input covariance and inflation factor.

Findings

Derived the largest high-SNR scaling factor for p.s.d. input covariance.

Proved zero inflation factor is optimal at low SNR.

Developed an iterative algorithm for joint optimization.

Abstract

The problem of dirty paper coding (DPC) over the (multi-antenna) fading dirty paper channel (FDPC) Y = H(X + S) + Z is considered when there is imperfect knowledge of the channel state information H at the transmitter (CSIT). The case of FDPC with positive definite (p.d.) input covariance matrix was studied by the authors in a recent paper, and here the more general case of positive semi-definite (p.s.d.) input covariance is dealt with. Towards this end, the choice of auxiliary random variable is modified. The algorithms for determination of inflation factor proposed in the p.d. case are then generalized to the case of p.s.d. input covariance. Subsequently, the largest DPC-achievable high-SNR (signal-to-noise ratio) scaling factor over the no-CSIT FDPC with p.s.d. input covariance matrix is derived. This scaling factor is seen to be a non-trivial generalization of the one achieved for the p.d. case. Next, in the limit of low SNR, it is proved that the choice of all-zero inflation factor (thus treating interference as noise) is optimal in the 'ratio' sense, regardless of the covariance matrix used. Further, in the p.d. covariance case, the inflation factor optimal at high SNR is obtained when the number of transmit antennas is greater than the number of receive antennas, with the other case having been already considered in the earlier paper. Finally, the problem of joint optimization of the input covariance matrix and the inflation factor is dealt with, and an iterative numerical algorithm is developed.