The number of connected wireless devices is anticipated to increase heavily in the next few years. Thus, there is a need for a new system which is able to handle billions of wireless devices. The category of massive multiple input multiple output (MIMO) systems is a great candidate for this purpose. Because of the large number of antennas in massive MIMO there is a need to reduce the dimension of the MIMO channel effectively to decrease the complexity. This could be achieved by using a particular prebeamforming technique that is introduced recently. An important aspect of wireless communication systems is the channel state information (CSI). In order to send and receive data through the channel, the transmitter and the receiver must know the CSI or at least have an estimation for it. In this thesis, different algorithms for estimating the channel vector coefficient and their performance are studied. Different approaches are used in order to find the best algorithm based on the performance of the estimating channel and the complexity of the algorithm. Also, algorithms are used to estimate the channel vector coefficient for different channel models.

An analysis of the diversity gain for bit-interleaved coded multiple beamforming (BICMB) method and BICMB with perfect coding (BICMB-PC) in millimeter-wave (mm-Wave) massive multiple-input multiple-output (MIMO) systems is carried out for both the single-user and multi-user scenarios. We show that full diversity gain and full spatial multiplexing order can be achieved in both scenarios. Also, we show that the diversity gain becomes independent of the number of transmitted data streams and the number of antennas in each remote antenna unit (RAU) at the transmitter and the receiver. Furthermore, in the multi-user scenario, the diversity gain is independent from the number of users in the system, when BICMB or BICMB-PC is utilized. Furthermore, we study BICMB with orthogonal frequency division multiplexing (OFDM) in order to achieve maximum diversity gain in mm-Wave massive MIMO distributed antenna subarray systems. The assumption here is that the channel state information (CSI) is known at both sides of the transmitter and the receiver and the number of antennas in each RAU tends to infinity. This latter assumption can be relaxed by a large number of antennas in each RAU, similar to the case for all massive MIMO research. Simulation results show that when the perfect CSI assumption is satisfied, the use of BICMB results in the diversity gain values predicted by the analysis.