PROJECT TITLE :
Estimation of Broadband Multiuser Millimeter Wave Massive MIMO-OFDM Channels by Exploiting Their Sparse Structure - 2018
In millimeter wave (mm-wave) huge multiple-input multiple-output (MIMO) systems, acquiring accurate channel state info is crucial for efficient beamforming (BF) and multiuser interference cancellation, which could be a difficult task since a coffee signal-to-noise ratio is encountered before BF in giant antenna arrays. The mm-wave channel exhibits a three-D clustered structure in the virtual angle of arrival (AOA), angle of departure (AOD), and delay domain that's imposed by the impact of power leakage, angular spread, and cluster period. We tend to extend the approximate message passing (AMP) with a nearest neighbor pattern learning algorithm for improving the attainable channel estimation performance, that adaptively learns and exploits the clustered structure in the 3-D virtual AOA-AOD-delay domain. The proposed technique is capable of approaching the performance bound described by the state evolution based on vector AMP framework, and our simulation results verify its superiority in mm-wave systems related to a broad bandwidth.
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