Generalized Superimposed Training Scheme in Cell-Free Massive MIMO Systems

Abstract

Regular pilots in a massive multi-input multi-output (MIMO) system with large number of users suffer from the pilot contamination effect due to limited training time. In this paper, for a cell-free massive MIMO system, we have proposed a generalized superimposed pilot (GSP) scheme, 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 and distributed time processing approach is employed, and the corresponding rate components are analyzed. It is shown that with careful design of precoding matrix 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 have been verified via simulations. It is also inferred that centralized processing in cell free system improves the data detection performance than localized processing. Iterative data detection at the central node also improves the MSE of data estimates. The pilot contamination effect, is significantly reduced due to availability of larger number of pilots, as compared to regular pilots transmission.