IRS-Based TDD Reciprocity Breaking for Pilot Decontamination in Massive MIMO
Xiliang Luo, Hanyu Zhu
Abstract
In this letter, we propose a novel way to mitigate the notorious pilot contamination in massive multiple-input multiple-output (MIMO) systems by exploiting the intelligent reflecting surfaces (IRSs). Particularly, we intentionally break the uplink (UL) and downlink (DL) channel reciprocity in time-division duplexing (TDD) systems by configuring the phases of the IRSs differently during the UL training phase and the DL data transmission phase. In this way, we show that the pilot contamination is mitigated and can be eliminated in some cases. Meanwhile, we analyze the signal-to-interference-plus-noise ratio (SINR) performance due to the non-reciprocal UL and DL channels. We further show the asymptotic optimality of our proposed decontamination scheme. The analytical results are also corroborated by numerical simulations.