Geniş bantlı masif MIMO sistemler için seyreklik haritası ve uzay-zaman güç profili kestirimine dayalı etkili hüzme ve kanal öğrenme algoritmaları.

In this thesis, an efficient beam and instantaneous channel acquisition scheme together with joint angle-delay power profile (JADPP), scatter map, construction are proposed for single-carrier (SC) mm-wave wideband sparse massive multiple-input multiple-output (MIMO) channels when hybrid beamforming architecture is utilized. We consider two different modes of operation, namely slow-time beam acquisition and fast-time instantaneous channel estimation, in training stage of time division duplex (TDD) based systems. In the first mode, 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 are obtained by using a novel two-stage 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 channel covariance matrices (CCMs) are formed with significantly reduced amount of training snapshots. Then, by using the estimated CCMs, the analog beamformer is reconstructed by means of a virtual sectorization (user-grouping via second-order channel statistics) while taking the inter-group and inter-symbol interference (ISI) into account. Finally, for the second mode of the training stage, two novel reduced rank instantaneous channel estimators, operating in a proper beamspace formed by the hybrid structure, are proposed. The proposed techniques attains the channel estimation accuracy of minimum mean square error (MMSE) filter with true knowledge of CCMs while reducing the training overhead considerably (compared to the conventional least-square (LS) type estimators).Thesis (M.S.) -- Graduate School of Natural and Applied Sciences. Electrical and Electronics Engineering