On Detection and Mitigation of Reused Pilots in Massive MIMO Systems

In a time-division duplex multiple antenna system the channel state information (CSI) can be estimated using reverse training. In multi-cell multi-user massive MIMO systems, pilot contamination degrades CSI estimation performance and adversely affects massive MIMO system performance. In this paper we consider a subspace-based semi-blind approach where we have training data as well as information bearing data from various users (both in-cell and neighboring cells) at the base station (BS). Existing semi-blind approaches assume that the interfering users from neighboring cells are always at distinctly lower power levels at the BS compared with the in-cell users. This requires (perfect) power control. In this paper we do not make any such assumption. Unlike existing approaches, the BS estimates the channels of all users: in-cell and significant neighboring cell users, i.e., ones with comparable power levels at the BS. We exploit both subspace method using correlation as well as blind source separation using higher-order statistics. Finally, the estimated channels are used to detect information symbols, which, in turn, are used as pseudo-pilots to re-estimate the in-cell users' channels. The proposed approach is illustrated via simulation examples and compared with some existing semi-blind methods.