Semi-Blind Channel-and-Signal Estimation for Uplink Massive MIMO With Channel Sparsity
share
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.
READ FULL TEXT
