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

Abstract

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 blocksparse 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.