An Efficient Spatial Channel Covariance Estimation via Joint Angle-Delay Power Profile in Hybrid Massive MIMO Systems

Kalayci A. O. , GÜVENSEN G. M.

IEEE International Conference on Communications (IEEE ICC) / Workshop on NOMA for 5G and Beyond, ELECTR NETWORK, 7 - 11 June 2020 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/iccworkshops49005.2020.9145120
  • Keywords: Channel covariance estimation, sparsity map, wideband massive MIMO, mm-wave, joint angle-delay power profile, CFAR thresholding, single-carrier transmission, hybrid beamforming, adaptive filtering, reduced rank channel estimation


In this paper, an efficient construction method for channel covariance matrices (CCMs) together with joint angle-delay power profile (JADPP) and sparsity map estimation is proposed for single-carrier (SC) mm-wave wideband massive multiple-input multiple-output (MIMO) channels when hybrid beamforming architecture is utilized. We consider slow-time beam acquisition mode for training stage of time division duplex (TDD) based systems where pre-structured hybrid beams are formed to scan intended angular sectors. The joint angle-delay sparsity map together with power intensities of each user channels is obtained by using a novel constant false alarm rate (CFAR) thresholding algorithm inspired from adaptive radar detection theory. The proposed thresholding algorithm employs a spatio-temporal adaptive matched filter (AMF) type estimator, taking the strong interference due to simultaneously active multipath components (MPCs) of different user channels into account, in order to estimate JADPP of each user. After applying the proposed thresholding algorithm on the estimated power profile, the angle-delay sparsity map of the massive MIMO channel is constructed, based on which the CCMs are formed with significantly reduced amount of training snapshots. The proposed techniques attain the channel estimation accuracy of minimum mean square error (MMSE) filter with true knowledge of CCMs. At the same time, they allow non-orthogonal pilot sequences among different users while reducing the training overhead (which is basically constant with the number of active users in the system) considerably.