  This paper considers the transceiver design for uplink massive multiple-input
multiple-output (MIMO) systems with channel sparsity in the angular domain.
Recent progress has shown that sparsity-learning-based blind signal detection
is able to retrieve the channel and data by using massage-passing based sparse
matrix factorization methods. Short pilots sequences are inserted into user
packets to eliminate the so-called phase and permutation ambiguities inherent
in sparse matrix factorization. In this paper, to exploit the knowledge of
these short pilot sequences more efficiently, we propose a semi-blind
channel-and-signal estimation (SCSE) scheme in which the knowledge of the pilot
sequences are integrated into the message passing algorithm for sparse matrix
factorization. The SCSE algorithm involves enumeration over all possible user
permutations, and so is time-consuming when the number of users is relatively
large. To reduce complexity, we further develop the simplified SCSE (S-SCSE) to
accommodate systems with a large number of users. We show that our semi-blind
signal detection scheme substantially outperforms the state-of-the-art blind
detection and training-based schemes in the short-pilot regime.
