Semi-Blind Channel-and-Signal Estimation for Uplink Massive MIMO With Channel Sparsity
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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 message-passing based noisy matrix factorization. We propose a semi-blind signal detection scheme in which a short pilot sequence is inserted into each user packet and the knowledge of pilots is integrated into the message passing algorithm for noisy matrix factorization. We derive a semi-blind channel and signal estimation (SCSE) algorithm based on the message-passing principles. 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 a 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.
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