Thesis (Ph.D.) - University of St Andrews, April 2009. / Restricted until 29th April 2011.

Thesis (M.A.)--Liberty University, 2010. / Includes bibliographical references.

Thesis--University of Wisconsin--Madison. / Typescript. Vita. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 426-441).

Thesis (Ph. M.)--University of Wisconsin--Madison, 1937. / Typescript. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references.

Thesis (Ph. D.)--Washington State University, May 2006. / Includes bibliographical references (leaves 176-208).

Thesis (Ph.D.)--Washington State University, May 2006. / Includes bibliographical references (p. 176-208).

Thesis (Ph. D.)--University of Notre Dame, 2004. / Thesis directed by Philip Gleason for the Department of History. "July 2004." Includes bibliographical references (leaves 408-438).

Thesis (M.A.)--Cincinnati Christian Seminary, 1987. / Abstract. Includes bibliographical references (leaves [113]-116).

The rapid growth of data hungry wireless applications has boosted the demand for wireless communication systems with improved reliability, wider coverage, and higher throughput. The main challenges facing the design of such systems are the limited resources, such as bandwidth, restricted transmission power, etc., and the impairments of the wireless channels, including fading effects, interference, and noise. Multiple-input multiple-output (MIMO) communication has been shown to be one of the most promising emerging wireless technologies that can efficiently enhance link reliability, improve system coverage, and boost the data transmission rate. Consequently, MIMO is now extensively adopted by many mainstream wireless industry standards, including 3GPP WCDMA/HSDPA, LTE, EVDO, WiFi, and WiMAX. By deploying multiple antennas at both transmitter and receiver sides, MIMO techniques license a new dimension (spatial dimension) that can be applied in various ways for combating the impairments of wireless networks. Furthermore, this new dimension has introduced a new concept known as Interference Alignment that can efficiently deal with the interference presentin the wireless communication networks. In particular, IA is highly attractive in terms of providing more degrees of freedom compared to techniques such as TDMA/FDMA. With this in mind, this thesis will focus on studying and developing advanced techniques and algorithms for reducing interference in cellular communication networks. The contributions of the thesis are as follows. Initially, a review is provided to reiterate some basic concepts of wireless communications and discuss the challenges faced by the techniques that deal with interference mitigation. Next, Chapter 3 presents a novel IA based cancellation scheme that is proposed for combating the interfering signals present in the compounded MIMO broadcast channels, where the users experience a multi-source transmission from several base stations. After defining the interference channel (IC) interference and X-channel interference, the partial transmit beamforming matrices of the closed-form downlink scheme alleviate the corresponding types of interference. Applying the proposed scheme allows one to treat the multi-cell network as a set of single-cell MIMO network, which leads to the simultaneous BER performance enhancement and data rate increase. Moreover, a generalization scheme is given to assign the appropriate antenna configuration for achieving maximum DoF. Furthermore, Chapter 4 demonstrates a comprehensive analysis on the number of DoF achievable by exploiting the transmit beamforming technique. Additionally, the proposed scheme is able to provide the maximum data rate under a certain antenna setting or compute a transmitter-receiver configuration in order to meet the required number of DoF. Chapter 5 considers a modified IA scheme for the compounded MIMO network when different classes of users communicate in the overlapped area. Due to various antenna settings of each receiver, the effect of spatial correlation on the achievable data rate is investigated. Moreover, an algorithm is derived for calculating the antenna configuration for different users classes. Then, the proposed scheme is extended for the case of Large-scale MIMO, which in turn provides sufficient insights into the impact of the deployment of a large number of antennas. Finally, Chapter 6 presents an alternative design of the IA scheme with no symbol extension for the cellular MIMO network. Subsequently, a modified eigenvalue-based scheme is proposed to enhance the overall system performance. Finally, the achievable data rate is calculated under different CSI acquisition scenarios. Chapter 7 concludes the thesis and provides a list of potential future work directions for further investigation.

Thesis (M.S.)—Boston University