Wireless communication systems utilize the wireless medium to perform over-the-air (OTA) data transfer. There are many factors that can impact the quality of wireless communications, such as medium imperfection, interfering environment, mismatch of transceivers, etc. To mitigate these problems and improve the quality of service (QoS), this research study is conducted on three important topics including synchronization techniques, impairment estimation theory and techniques, and interference alignment techniques. In this thesis, it firstly present a dual link algorithm to align and manage the interference of multiple-input and multiple-output (MIMO) networks. A field-programmable gate array (FPGA) prototype is designed for software defined radio (SDR) platforms. As one of the key components, a hardware efficient architecture is proposed for the implementation of singular value decomposition (SVD). Secondly, it proposes a maximum-likelihood (ML) based synchronization approach for carrier frequency synchronization for MIMO systems. The algorithm is also implemented on FPGA for real-time performance evaluation. Finally, as an exemplary study of machine learning techniques for wireless communications, a neural network (NN) based estimator is proposed to perform coarse frequency offset estimations for MIMO systems. The proposed NN based estimator can accommodate various channel models and the results show promising performance in terms of accuracy and estimation range. In summary, this thesis provides a comprehensive study on interference alignment, carrier synchronization, and impairment estimation using different approaches. Efficient hardware implementations for the key algorithms are also presented.

Wireless communication systems utilize the wireless medium to perform over-the-air (OTA) data transfer. There are many factors that can impact the quality of wireless communications, such as medium imperfection, interfering environment, mismatch of transceivers, etc. To mitigate these problems and improve the quality of service (QoS), this research study is conducted on three important topics including synchronization techniques, impairment estimation theory and techniques, and interference alignment techniques. In this thesis, it firstly present a dual link algorithm to align and manage the interference of multiple-input and multiple-output (MIMO) networks. A field-programmable gate array (FPGA) prototype is designed for software defined radio (SDR) platforms. As one of the key components, a hardware efficient architecture is proposed for the implementation of singular value decomposition (SVD). Secondly, it proposes a maximum-likelihood (ML) based synchronization approach for carrier frequency synchronization for MIMO systems. The algorithm is also implemented on FPGA for real-time performance evaluation. Finally, as an exemplary study of machine learning techniques for wireless communications, a neural network (NN) based estimator is proposed to perform coarse frequency offset estimations for MIMO systems. The proposed NN based estimator can accommodate various channel models and the results show promising performance in terms of accuracy and estimation range. In summary, this thesis provides a comprehensive study on interference alignment, carrier synchronization, and impairment estimation using different approaches. Efficient hardware implementations for the key algorithms are also presented.

The thesis deals with the analysis and the reception of the freely available signals of the navigation satellites in the L1 and E1 bands of the GPS and Galileo systems. The described signal reception sections include the process of the acquisition, the carrier frequency and phase synchronization and tracking, the spreading code phase tracking, the signal demodulation and the channel decoding. The simulation of the entire receiver is performed in MATLAB. The deeply analyzed signal reception component is the one responsible for the carrier phase and frequency synchronization and tracking. In that case, more methods and their comparison are usually listed. The signal reception component, which is responsible for the carrier phase and frequency tracking and the spreading code phase tracking, is also implemented in FPGA.