Generalized Superimposed Training Scheme in IRS-Assisted Cell-Free Massive MIMO Systems
Navneet Garg, Hanxiao Ge, Tharmalingam Ratnarajah
Abstract
In this article, for a cell-free massive multi-input multi-output (MIMO) system assisted with intelligent reflective surface (IRS) panels, a generalized superimposed pilot (GSP) training scheme is proposed, where the available number of pilots are equal to the coherence time slots, and the transmitting data symbols are spread over the coherence time with the help of simple precoding. Further, in order to keep the system scalable, a low complexity approach is employed for processing, and the corresponding rate components are analyzed. It is shown that with careful design of precoding and number of data symbols, the GSP symbols can provide much better channel estimation and data detection performance, as compared to the regular pilot scheme and the conventional superimposed scheme. These results, verified via simulations, shows that the centralized processing improves the data detection performance than localized processing. The pilot contamination effect, is significantly reduced due to the availability of larger number of pilots, as compared to the regular pilots transmission. For four IRS panels in the system, the proposed scheme is shown to reduce the channel estimation errors by 74% approximately.