A compressive sensing-maximum likelihood approach for off-grid wideband channel estimation at mmWave

Abstract

Obtaining accurate channel state information is crucial to configure the antenna arrays and the digital precoders and combiners in hybrid millimeter wave (mmWave) MIMO architectures. Most of prior work on channel estimation with hybrid MIMO architectures relies on the use of finite-resolution dictionaries to estimate angles of arrival (AoA) and angles of departure (AoD). When the AoAs or AoDs do not fall within the quantization grids used to generate these dictionaries, there is an unavoidable grid error in the estimation of the channel. In this paper, we propose a mixed compressed sensing-maximum likelihood algorithm that uses continuous dictionaries to estimate the channel. The quantization error due to using finite resolution dictionaries can be neglected with this approach, enhancing estimation performance without resorting to very large dictionaries. Simulation results show how the new algorithm outperforms approaches based on finite resolution dictionaries previously proposed for the estimation of mmWave channels.