Sparse Regression Codes for MIMO Detection

We consider sparse regression codes (SPARCs) for the multiple-input multiple-output (MIMO) detection problem. Specifically, we introduce normals with unknown variances (NUV) priors to represent one-hot vectors in SPARCs and derive the corresponding NUV-EM algorithm accordingly. Then, we apply this algorithm to MIMO detection problems. In order to tackle issues arising from the proposed NUV-EM algorithm, a (Hadamard-based) Gaussian generalized approximate message passing (GAMP) algorithm, along with a simple rejection technique, is proposed. Simulation results show that our proposed algorithms work well over various channels.