Transmit Optimization with Improper Gaussian Signaling for Interference Channels

TL;DR

This paper explores how improper Gaussian signaling can enhance achievable rates in interference channels by optimizing transmit covariance and pseudo-covariance matrices, introducing widely linear precoding and joint optimization algorithms.

Contribution

It introduces improper Gaussian signaling and widely linear precoding for interference channels, providing algorithms that outperform traditional proper Gaussian signaling.

Findings

Improper Gaussian signaling improves achievable rates.

Joint covariance and pseudo-covariance optimization achieves Pareto-optimal rates.

Separate optimization guarantees rate improvement over proper signaling.

Abstract

This paper studies the achievable rates of Gaussian interference channels with additive white Gaussian noise (AWGN), when improper or circularly asymmetric complex Gaussian signaling is applied. For the Gaussian multiple-input multiple-output interference channel (MIMO-IC) with the interference treated as Gaussian noise, we show that the user's achievable rate can be expressed as a summation of the rate achievable by the conventional proper or circularly symmetric complex Gaussian signaling in terms of the users' transmit covariance matrices, and an additional term, which is a function of both the users' transmit covariance and pseudo-covariance matrices. The additional degrees of freedom in the pseudo-covariance matrix, which is conventionally set to be zero for the case of proper Gaussian signaling, provide an opportunity to further improve the achievable rates of Gaussian MIMO-ICs by employing improper Gaussian signaling. To this end, this paper proposes widely linear precoding, which efficiently maps proper information-bearing signals to improper transmitted signals at each transmitter for any given pair of transmit covariance and pseudo-covariance matrices. In particular, for the case of two-user Gaussian single-input single-output interference channel (SISO-IC), we propose a joint covariance and pseudo-covariance optimization algorithm with improper Gaussian signaling to achieve the Pareto-optimal rates. By utilizing the separable structure of the achievable rate expression, an alternative algorithm with separate covariance and pseudo-covariance optimization is also proposed, which guarantees the rate improvement over conventional proper Gaussian signaling.