Subspace-Based Semi-Blind Channel Estimation for User-Centric Cell-Free Massive MIMO Systems

Recently, the user-centric (UC) cell-free (CF) massive multiple-input multiple-output (MIMO) system has emerged as one of the most promising enablers for future wireless networks. However, with the increase of the number of user equipments (UEs) in the network, pilot reuse for channel estimation causes interference, referred to as pilot contamination. In this paper, a subspace-based semi-blind channel estimation scheme is proposed for UC CF massive MIMO systems. By exploiting the uplink data signal received at each access point (AP), eigenvalue-decomposition (EVD) is employed on the covariance matrix of received signal to obtain the signal subspace. Based on the channel gains and pilot assignment results for each UE, a subspace selection scheme is developed to separate target signal from interference in subspace, which is followed by subspace projection to mitigate the pilot contamination. Simulations results verify the effectiveness of the proposed scheme in improving the uplink channel estimation accuracy of UC CF systems even without the prior knowledge of channel covariance matrix.