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In this paper, we study blind channel-and-signal estimation by exploiting theburst-sparse structure of angular-domain propagation channels in massive MIMOsystems. The state-of-the-art approach utilizes the structured channel sparsityby sampling the angular-domain channel representation with a uniformangle-sampling grid, a.k.a. virtual channel representation. However, thisapproach is only applicable to uniform linear arrays and may cause asubstantial performance loss due to the mismatch between the virtualrepresentation and the true angle information. To tackle these challenges, wepropose a sparse channel representation with a super-resolution sampling gridand a hidden Markovian support. Based on this, we develop a novel approximateinference based blind estimation algorithm to estimate the channel and the usersignals simultaneously, with emphasis on the adoption of theexpectation-maximization method to learn the angle information. Furthermore, wedemonstrate the low-complexity implementation of our algorithm, making use offactor graph and message passing principles to compute the marginal posteriors.Numerical results show that our proposed method significantly reduces theestimation error compared to the state-of-the-art approach under varioussettings, which verifies the efficiency and robustness of our method.

Super-Resolution Blind Channel-and-Signal Estimation for Massive MIMO with One-Dimensional Antenna Array