Symbol Level Precoding for Systems with Improper Gaussian Interference

TL;DR

This paper investigates symbol-level precoding in multi-antenna systems with improper Gaussian interference, proposing robust designs that improve error rates and energy efficiency under uncertainty.

Contribution

It introduces robust precoding methods accounting for IGI statistics, including cases with unknown channel and interference properties, and analyzes worst-case scenarios.

Findings

SLP outperforms BLP in SER and EE

Worst-case IGI is proper for BLP, maximally improper for SLP

Robust designs improve performance under uncertainty

Abstract

This paper focuses on precoding design in multi-antenna systems with improper Gaussian interference (IGI), characterized by correlated real and imaginary parts. We first study block level precoding (BLP) and symbol level precoding (SLP) assuming the receivers apply a pre-whitening filter to decorrelate and normalize the IGI. We then shift to the scenario where the base station (BS) incorporates the IGI statistics in the SLP design, which allows the receivers to employ a standard detection algorithm without pre-whitenting. Finally we address the case where the channel and statistics of the IGI are unknown, and we formulate robust BLP and SLP designs that minimize the worst case performance in such settings. Interestingly, we show that for BLP, the worst-case IGI is in fact proper, while for SLP the worst case occurs when the interference signal is maximally improper, with fully correlated real and imaginary parts. Numerical results reveal the superior performance of SLP in terms of symbol error rate (SER) and energy efficiency (EE), especially for the case where there is uncertainty in the non-circularity of the jammer.