[Thesis]. Manchester, UK: The University of Manchester; 2016.
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.