Spatially correlated channel estimation based on block iterative support detection for massive MIMO systems

Downlink channel estimation with low pilot overhead is an important and challenging problem in massive multiple-input-multiple-output (MIMO) systems due to the substantially increased MIMO channel dimension. A block iterative support detection (block-ISD)-based algorithm for downlink channel estimation to reduce the pilot overhead is proposed, which is achieved by fully exploiting the block sparsity inherent in the block-sparse equivalent channel derived from the spatial correlations of MIMO channels. Furthermore, unlike conventional compressive sensing (CS) algorithms that rely on prior knowledge of the sparsity level, block-ISD relaxes this demanding requirement and is thus more practically appealing. Simulation results demonstrate that block-ISD yields better normalised mean square error (NMSE) performance than classical CS algorithms, and achieve a reduction of 84% pilot overhead compared with conventional channel estimation techniques.