Browsing by Author "Aygün, Bengi"

Now showing 1 - 1 of 1

Capacity bounds and channel estimation for MIMO relay channels with covariance feedback at the transmitters
(Bahçeşehir Üniversitesi Fen Bilimleri Enstitüsü, 2012-09) Aygün, Bengi; Soysal, Alkan
In this thesis, some resource allocation problems for fading multiple input multiple output (MIMO) relay channels are considered, where decode and forward (DF) relay strategy is used. In our model, the transmitters have partial channel state information (CSI). The receivers are assumed to have prefect channel state in some parts of the thesis, while channel estimation errors are taken into account in others parts of the thesis. The resource allocation problems are in terms of finding optimum transmission parameters (like average transmission powers, the source and relay transmit covariance matrices) and channel estimation parameters (like training duration, training sequence and training power). In the first part of the thesis, the transmitters have partial CSI, while the receivers have perfect CSI. The capacity of this channel is not known, however, we obtain lower and upper bounds to the capacity for both half-duplex and full-duplex transmission. These bounds require a joint optimization over the source and relay transmit covariance matrices. The methods utilized in the previous literature cannot handle this joint optimization over the transmit covariance matrices for the system model considered in this thesis. First, we propose a sub-optimal solution by solving the source and relay transmit covariance matrices consecutively, i.e., not jointly. This suboptimal solution make use of the previous literature and finds the eigenvectors of the transmit covariance matrices before proposing the algorithm that finds the eigenvalues of the transmit covariance matrices. Then, in order to solve the joint optimization problem, we utilize matrix differential calculus and propose iterative algorithms that find the transmit covariance matrices of source and relay nodes. In this method, there is no need to specify the eigenvectors of the transmit covariance matrices first. The algorithm updates both the eigenvectors and the eigenvalues at each iteration. Through simulations, we observe that lower and upper bounds are close to each other. However, the distance between the lower and the upper bound depends on the channel conditions. In the fifth part of the thesis, the transmitters have partial CSI, while the receivers experience channel estimation errors. The capacity of this channel is also not known, however, we obtain lower and upper bounds to the capacity. These bounds require joint optimization over not only the source and relay transmit covariance matrices, but also training sequence matrix. We deal with the trade-off between estimating the channel better and increasing the channel rate. We use minimum mean square error to minimize the estimation error.