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In this paper, we study joint antenna activity detection, channel estimation,and multiuser detection for massive multiple-input multiple-output (MIMO)systems with general spatial modulation (GSM). We first establish adouble-sparsity massive MIMO model by considering the channel sparsity of themassive MIMO channel and the signal sparsity of GSM. Based on thedouble-sparsity model, we formulate a blind detection problem. To solve theblind detection problem, we develop message-passing based blindchannel-and-signal estimation (BCSE) algorithm. The BCSE algorithm basicallyfollows the affine sparse matrix factorization technique, but with criticalmodifications to handle the double-sparsity property of the model. We show thatthe BCSE algorithm significantly outperforms the existing blind andtraining-based algorithms, and is able to closely approach the genie bounds(with either known channel or known signal). In the BCSE algorithm, shortpilots are employed to remove the phase and permutation ambiguities aftersparse matrix factorization. To utilize the short pilots more efficiently, wefurther develop the semi-blind channel-and-signal estimation (SBCSE) algorithmto incorporate the estimation of the phase and permutation ambiguities into theiterative message-passing process. We show that the SBCSE algorithmsubstantially outperforms the counterpart algorithms including the BCSEalgorithm in the short-pilot regime.

Double-Sparsity Learning Based Channel-and-Signal Estimation in Massive MIMO with Generalized Spatial Modulation