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.
| Identifer | oai:union.ndltd.org:wpi.edu/oai:digitalcommons.wpi.edu:etd-dissertations-1624 |
| Date | 10 December 2019 |
| Creators | Zhou, Mingda |
| Contributors | Xinming Huang, Advisor, Kaveh Pahlavan, Committee Member, Youjian Liu, Committee Member |
| Publisher | Digital WPI |
| Source Sets | Worcester Polytechnic Institute |
| Detected Language | English |
| Type | text |
| Source | Doctoral Dissertations (All Dissertations, All Years) |
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