<p> In this thesis, we consider a multiuser downlink OFDM system for which the channel state information ( CSI) is known to both the transmitter and the receiver. </p> <p> For such a system, we design an optimal precoder that minimizes the total mean square error (MSE) subject to a total power constraint for which a minimum MSE (MMSE) equalizer is employed. We show that, the MMSE precoder can be obtained by optimally allocating the subcarriers and optimally allocating the power. This problem can be solved by a two-stage process, in which we minimize the lower bound of the MSE to obtain the optimal power for each subcarrier, followed by seeking an optimal precoder to achieve this minimized lower bound. Specifically, our subcarrier allocation strategy states that, each subcarrier should be allocated to only one user that has the largest subchannel gain in that subcarrier. </p> <P> Moreover, based on this subcarrier allocation strategy, we perform an optimal power loading and design the corresponding optimal precoder that minimizes the average bit error rate (BER). Here, the MMSE equalizer is also employed. This optimization problem is solved by two stages. In the first stage, we derive the lower bound of the average BER and minimize this lower bound. After we employ the MMSE subcarrier allocation strategy, the optimal power loading problem can be efficiently solved by interior point methods. In order to reduce computation complexity, an alternative, efficient power loading method is proposed here, which is much more efficient when the number of subcarriers is large. In the second stage, to achieve the minimized lower bound, we seek a design of an optimal precoder. Simulation results show that for moderate to high signal-to-noise ratio (SNR), the performance of the minimum BER {MBER) precoder employed with the MMSE equalizer design is superior to several other design methods, including the MMSE precoder design. </p> / Thesis / Master of Applied Science (MASc)

Massive MU-MIMO (Multiuser-Multiple Input and Multple Output) is a promising technology for 5G wireless communications because of its spectrum and energy efficiency. To combat the distortion from multipath fading channel, the acquisition of channel state information is essential, which generally requires the training signal that lowers the data rate. In addition, coarse quantization can reduce the high computational energy and cost, yet results in the loss of information. In this thesis, an iterative decision feedback receiver, including iterative Channel Estimation (CE) and equalization, is constructed for a Massive MU-MIMO uplink system. The impact of multipath distortion and coarse quantization can be gradually reduced due to the iterative structure that exploits extrinsic feedback to improve the CE and data detection, so that the data rate is improved by reducing training signals for CE and by using low precision quantization. To observe and evaluate the convergence behaviour, an Extrinsic Information Transfer (EXIT) chart method is utilized to visualize the performance of the iterative receiver. / Graduate

This paper describes a wireless communication model based on IEEE 802.11n. Typical methods for channel equalisation and estimation are described, such as the least squares method and the minimum mean square error method. Equalization based on deep learning was used as well. Coded and uncoded bit error rate was used as a performance identifier. Experiments with topology of the neural network has been performed. Programming languages such as MATLAB and Python were used in this work.