Millimeter wave communication (mmWC) over Eband- frequencies between 30 and 300 GHz- is a promising volunteer for 5G indoor/outdoor communication systems with an ultra high data rates thanks to the large bandwidth. To subdue the channel propagation characteristics in this frequency band, high dimensional antenna arrays need to be deployed at both the base station (BS) and mobile sets (MS). Due to large number of antennas at transceiver, mmW systems avoid to employ ADC or RF chain in each branch of MIMO system because of power constraints. Thus Such systems leverage to the hybrid analog/digital precoding/combining architecture for downlink single user deployment instead of strict digital process. Large array at both BS and MS inside of analog beamforming requires developed channel estimation. This paper propose a new algorithm to estimate the mmW channel by exploiting the sparse nature of the channel and finding the subspace of received signal vectors based on MUSIC. By combining the multiple measurement vector (MMV) concept and MISIC, a modified approach tries to recover support of an unknown channel matric accurately even under the defective- rank condition. Simulation results indicate MUSIC-based approaches offer lower estimation error and higher sum rates compared with conventional MMV solutions.

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