The scarce and overpopulated radio spectrum is going to present a major barrier to the growth and development of future wireless networks. As such, spectrum sharing seems to be inevitable to accommodate the exploding demand for high data rate applications. A major challenge to realizing the potential advantages of spectrum sharing is interference management. This thesis deals with interference management techniques in noncooperative networks. In specific, interference alignment is used as a powerful technique for interference management. We use the degrees of freedom (DoF) as the figure of merit to evaluate the performance improvement due to the interference management schemes. This dissertation is organized in two parts. In the first part, we consider the K-user multiple input multiple output (MIMO) Gaussian interference channel (IC) with M antennas at each transmitter and N antennas at each receiver. This channel models the interaction between K transmitter-receiver pairs sharing the same spectrum for data communication. It is assumed that the channel coefficients are constant and are available at all nodes prior to data transmission. A new cooperative upper-bound on the DoF of this channel is developed which outperforms the known bounds. Also, a new achievable transmission scheme is provided based on the idea of interference alignment. It is shown that the achievable DoF meets the upper-bound when the number of users is greater than a certain threshold, and thus it reveals the channel DoF. In the second part, we consider communication over MIMO interference and X channels in a fast fading environment. It is assumed that the transmitters obtain the channel state information (CSI) after a finite delay which is greater than the coherence time of the channel. In other words, the CSI at the transmitters becomes outdated prior to being exploited for the current transmission. New transmission schemes are proposed which exploit the knowledge of the past CSI at the transmitters to retrospectively align interference in the subsequent channel uses. The proposed transmission schemes offer DoF gain compared to having no CSI at transmitters. The achievable DoF results are the best known results for these channels. Simple cooperative upper-bounds are developed to prove the tightness of our achievable results for some network configurations.

Research in wireless communications has been actively carried out in recent years. In order to enable a high data transmission rate, multiple-input multiple-output (MIMO) communications has been proposed and commonly adopted. Accurate channel identification and reliable data detection are major challenges in the implementation of a communications system operating over a wireless fading channel. These issues become even more challenging in MIMO systems since there are many more parameters involved in the estimation processes. This thesis, consisting of four major parts, focuses on applying convex optimization to solve design problems in both MIMO channel estimation and data detection. The first part proposes a novel orthogonal affine precoding technique for jointly optimal channel estimation and symbol detection in a general MIMO frequency-selective fading channel. Additionally, the optimal power allocation between the data and training signals is also analytically derived. The proposed technique is shown to perform much better than other affine precoding techniques in terms of detection error probability and computational complexity. The second part is concerned with the MIMO orthogonal frequency-division multiplexing (OFDM) systems. The superimposed training technique developed in the first part is applied and extended for MIMO-OFDM systems where all the involved transmitters and receivers are assumed to be uncorrelated. Analytical and numerical results confirm that the proposed design can efficiently identify the unknown wireless channel as well as effectively recover the data symbols, while conserving the transmission bandwidth. The third part considers training and precoding designs for OFDM under colored noise environment. The superiority of the proposed design over the previously-known design under colored noise is thoroughly demonstrated. The last part of the thesis develops the orthogonal affine precoder for spatially correlated MIMO-OFDM systems. The optimal superimposed training sequences are solved by tractable semi-definite programming. To have a better computational efficiency, two approximate design techniques are also presented. Furthermore, the non-redundancy precoder proposed in the third part is employed to combat channel correlation. As a result, the proposed designs are demonstrated to outperform other known designs in terms of channel estimation and data detection.

Drug development is a long and expensive process. It is often not until potential drug candidates are administered to humans that accurate quantification of their pharmacokinetic characteristics is achieved. The goal of developing quantitative structure-pharmacokinetic relationships (QSPkRs) is to relate the molecular structure of a chemical entity with its pharmacokinetic characteristics. In this thesis artificial neural networks (ANNs) were used to construct in silico predictive QSPkRs for various pharmacokinetic parameters using different drug data sets. Drug pharmacokinetic data for all studies were taken from the literature. Information for model construction was extracted from drug molecular structure. Numerous theoretical descriptors were generated from drug structure ranging from simple constitutional and functional group counts to complex 3D quantum chemical numbers. Subsets of descriptors were selected which best modeled the target pharmacokinetic parameter(s). Using manual selective pruning, QSPkRs for physiological clearances, volumes of distribution, and fraction bound to plasma proteins were developed for a series of beta-adrenoceptor antagonists. All optimum ANN models had training and cross-validation correlations close to unity, while testing was performed with an independent set of compounds. In most cases the ANN models developed performed better than other published ANN models for the same drug data set. The ability of ANNs to develop QSPkRs with multiple target outputs was investigated for a series of cephalosporins. Multilayer perceptron ANN models were constructed for prediction of half life, volume of distribution, clearances (whole body and renal), fraction excreted in the urine, and fraction bound to plasma proteins. The optimum model was well able to differentiate compounds in a qualitative manner while quantitative predictions were mostly in agreement with observed literature values. The ability to make simultaneous predictions of important pharmacokinetic properties of a compound made this a valuable model. A radial-basis function ANN was employed to construct a quantitative structure-bioavailability relationship for a large, structurally diverse series of compounds. The optimum model contained descriptors encoding constitutional through to conformation dependent solubility characteristics. Prediction of bioavailability for the independent testing set were generally close to observed values. Furthermore, the optimum model provided a good qualitative tool for differentiating between drugs with either low or high experimental bioavailability. QSPkR models constructed with ANNs were compared with multilinear regression models. ANN models were shown to be more effective at selecting a suitable subset of descriptors to model a given pharmacokinetic parameter. They also gave more accurate predictions than multilinear regression equations. This thesis presents work which supports the use of ANNs in pharmacokinetic modeling. Successful QSPkRs were constructed using different combinations of theoretically-derived descriptors and model optimisation techniques. The results demonstrate that ANNs provide a valuable modeling tool that may be useful in drug discovery and development.

Visible light communication (VLC) is an emerging form of optical wireless communication that transmits data by modulating light in the visible spectrum. To meet the growing demand for wireless communication capacity from mobile devices, we investigate multiple-input multiple-output (MIMO) VLC to achieve multiplexing capacity gains and to allow multiple users to simultaneously transmit without disrupting each other. Previous approaches to receive VLC signals have either been unable to simultaneously receive multiple independent signals from multiple transmitters, unable to adapt to moving transmitters and receivers, or unable to sample the received signals fast enough for high-speed VLC. In this dissertation, we develop and evaluate two novel approaches to receive high-speed MIMO VLC signals from mobile transmitters that can be practically scaled to support additional transmitters. The first approach, Token-Based Pixel Selection (TBPS) exploits the redundancy and sparsity of high-resolution transmitter images in imaging VLC receivers to greatly increase the rate at which complementary metal-oxide semiconductor (CMOS) active pixel sensor (APS) image sensors can sample VLC signals though improved signal routing to enable such high-resolution image sensors to capture high-speed VLC signals. We further model the CMOS APS pixel as a linear shift-invariant system, investigate how it scales to support additional transmitters and higher resolutions, and investigate how noise can affect its performance. The second approach, a spatial light modulator (SLM)-based VLC receiver, uses an SLM to dynamically control the resulting wireless channel matrix to enable relatively few photodetectors to reliably receive from multiple transmitters despite their movements. As part of our analysis, we develop a MIMO VLC channel capacity model that accounts for the non-negativity and peak-power constraints of VLC systems to evaluate the performance of the SLM VLC receiver and to facilitate the optimization of the channel matrix through the SLM.

Abstract Advanced multiple-input multiple-output (MIMO) transceiver structures which utilize the knowledge of channel state information (CSI) at the transmitter side to optimize certain link parameters (e.g., throughput, fairness, spectral efficiency, etc.) under different constraints (e.g., maximum transmitted power, minimum quality of services (QoS), etc.) are considered in this thesis. Adaptive transmission schemes for point-to-point MIMO systems are considered first. A robust link adaptation method for time-division duplex systems employing MIMO-OFDM channel eigenmode based transmission is developed. A low complexity bit and power loading algorithm which requires low signaling overhead is proposed. Two algorithms for computing the sum-capacity of MIMO downlink channels with full CSI knowledge are derived. The first one is based on the iterative waterfilling method. The convergence of the algorithm is proved analytically and the computer simulations show that the algorithm converges faster than the earlier variants of sum power constrained iterative waterfilling algorithms. The second algorithm is based on the dual decomposition method. By tracking the instantaneous error in the inner loop, a faster version is developed. The problem of linear transceiver design in MIMO downlink channels is considered for a case when the full CSI of scheduled users only is available at the transmitter. General methods for joint power control and linear transmit and receive beamformers design are provided. The proposed algorithms can handle multiple antennas at the base station and at the mobile terminals with an arbitrary number of data streams per scheduled user. The optimization criteria are fairly general and include sum power minimization under the minimum signal-to-interference-plus-noise ratio (SINR) constraint per data stream, the balancing of SINR values among data streams, minimum SINR maximization, weighted sum-rate maximization, and weighted sum mean square error minimization. Besides the traditional sum power constraint on the transmit beamformers, multiple sum power constraints can be imposed on arbitrary subsets of the transmit antennas.This extends the applicability of the results to novel system architectures, such as cooperative base station transmission using distributed MIMO antennas. By imposing per antenna power constraints, issues related to the linearity of the power amplifiers can be handled as well. The original linear transceiver design problems are decomposed as a series of remarkably simpler optimization problems which can be efficiently solved by using standard convex optimization techniques. The advantage of this approach is that it can be easily extended to accommodate various supplementary constraints such as upper and/or lower bounds for the SINR values and guaranteed QoS for different subsets of users. The ability to handle transceiver optimization problems where a network-centric objective (e.g., aggregate throughput or transmitted power) is optimized subject to user-centric constraints (e.g., minimum QoS requirements) is an important feature which must be supported by future broadband communication systems.

Spatial Modulation (SM) is a Multiple-Input Multiple-Output (MIMO) transmission technique which realizes low complexity implementations in wireless communication systems. Due the transmission principle of SM, only one Radio Frequency (RF) chain is required in the transmitter. Therefore, the complexity of the transmitter is lower compared to the complexity of traditional MIMO schemes, such as Spatial MultipleXing (SMX). In addition, because of the single RF chain configuration of SM, only one Power Amplifier (PA) is required in the transmitter. Hence, SM has the potential to exhibit significant Energy Efficiency (EE) benefits. At the receiver side, due to the SM transmission mechanism, detection is conducted using a low complexity (single stream) Maximum Likelihood (ML) detector. However, despite the use of a single stream detector, SM achieves a multiplexing gain. A point-to-point closed-loop variant of SM is receive space modulation. In receive space modulation, the concept of SMis extended at the receiver side, using linear precoding with Channel State Information at the Transmitter (CSIT). Even though receive space modulation does not preserve the single RF chain configuration of SM, due to the deployed linear precoding, it can be efficiently incorporated in a Space Division Multiple Access (SDMA) or in a Virtual Multiple-Input Multiple-Output (VMIMO) architecture. Inspired by the potentials of SM, the objectives of this thesis are the evaluation of the EE of SM and its extension in different forms of MIMO communication. In particular, a realistic power model for the power consumption of a Base Station (BS) is deployed in order to assess the EE of SM in terms of Mbps/J. By taking into account the whole power supply of a BS and considering a Time Division Multiple Access (TDMA) multiple access scheme, it is shown that SM is significantly more energy efficient compared to the traditional MIMO techniques. In the considered system setup, it is shown that SM is up to 67% more energy efficient compared to the benchmark systems. In addition, the concept of space modulation is researched at the receiver side. Specifically, based on the union bound technique, a framework for the evaluation of the Average Bit Error Probability (ABEP), diversity order, and coding gain of receive space modulation is developed. Because receive space modulation deploys linear precoding with CSIT, two new precoding methods which utilize imperfect CSIT are proposed. Furthermore, in this thesis, receive space modulation is incorporated in the broadcast channel. The derivation of the theoretical ABEP, diversity order, and coding gain of the new broadcast scheme is provided. It is concluded that receive space modulation is able to outperform the corresponding traditional MIMO scheme. Finally, SM, receive space modulation, and relaying are combined in order to form a novel virtual MIMO architecture. It is shown that the new architecture practically eliminates or reduces the problem of the inefficient relaying of the uncoordinated virtual MIMO space modulation architectures. This is undertaken by using precoding in a novel fashion. The evaluation of the new architecture is conducted using simulation and theoretical results.

Long-range surface plasmon-polaritons are transverse-magnetic polarized optical surface waves formed through the interaction of photons with free electrons at the surface of metal slabs or stripes. They play important roles in a variety of field such as integrated optics, amplifiers and lasers, optical sensing, modulation, etc. Due to their longer propagation length and deeper penetration depth compared to those of single-interface surface plasmon-polaritons, they have become increasingly promising in optical sensing. In sensing applications, it is necessary to reduce the noise level in order to obtain a lower detection limit. One way to achieve this is to use dual- or triple-output Mach-Zehnder interferometers so that the common perturbations among the outputs can be suppressed. The objective of this thesis is to provide deeper insights on the performances of dual- and triple-output Mach-Zehnder interferometers in thermo-optic and optical bulk sensing applications, theoretically and experimentally, and to demonstrate their ability to suppress common perturbations and lower the detection limit. On the theoretical side, the objective is approached by constructing a model for the transfer characteristic. For dual-output Mach-Zehnder interferometers, the plane-wave model is used to develop a general model for thermo-optic sensing and an unbalanced model for optical bulk sensing. For triple-output ones, local normal mode theory is used with modal analysis for the 3×3 coupler portion of the structure. Quantitative methods to analyze and compare different detection schemes are developed. The minimum detectable phase shift is determined for the case of thermo-optic sensing while the detection limit is determined for optical bulk sensing. On the experimental side, the objective is approached by providing a direct experimental demonstration of the transfer characteristics at an optimized operating wavelength for the coupler portion of the device, then comparing to theory. Time traces are carried out and various detection schemes are applied to suppress common perturbations among the outputs, and to improve the minimum detectable phase shift or the detection limit.

Researchers have shown that by combining multiple input multiple output (MIMO) techniques with CDMA then higher gains in capacity, reliability and data transmission speed can be attained. But a major drawback of MIMO-CDMA systems is multiple access interference (MAI) which can reduce the capacity and increase the bit error rate (BER), so statistical analysis of MAI becomes a very important factor in the performance analysis of these systems. In this thesis, a detailed analysis of MAI is performed for binary phase-shift keying (BPSK) signals with random signature sequence in Raleigh fading environment and closed from expressions for the probability density function of MAI and MAI with noise are derived. Further, probability of error is derived for the maximum Likelihood receiver. These derivations are verified through simulations and are found to reinforce the theoretical results. Since the performance of MIMO suffers significantly from MAI and inter-symbol interference (ISI), equalization is needed to mitigate these effects. It is well known from the theory of constrained optimization that the learning speed of any adaptive filtering algorithm can be increased by adding a constraint to it, as in the case of the normalized least mean squared (NLMS) algorithm. Thus, in this work both linear and non-linear decision feedback (DFE) equalizers for MIMO systems with least mean square (LMS) based constrained stochastic gradient algorithm have been designed. More specifically, an LMS algorithm has been developed , which was equipped with the knowledge of number of users, spreading sequence (SS) length, additive noise variance as well as MAI with noise (new constraint) and is named MIMO-CDMA MAI with noise constrained (MNCLMS) algorithm. Convergence and tracking analysis of the proposed algorithm are carried out in the scenario of interference and noise limited systems, and simulation results are presented to compare the performance of MIMO-CDMA MNCLMS algorithm with other adaptive algorithms.

This dissertation introduces three novel multiuser detection approaches in Multiple Input Multiple Output (MIMO) Orthogonal Frequency Division Multiplexing (OFDM) systems by blind signal separation (BSS) techniques. The conventional methodologies for multiuser detection have to retransmit channel state information (CSI) constantly from the transmitter in MIMO ODFM systems at the cost of economic efficiency, because they require more channel resources to improve the communication quality. Compared with the traditional methodologies, the proposed BSS methods are relatively efficient approaches without the unnecessary retransmission of channel state information. The current methodologies apply the space-time coding or the spatial multiplexing to implement an MIMO OFDM system, which requires relatively complex antenna design and allocation in the transmitter. The proposed Spatial Division Multiple Access (SDMA) method enables different mobile users to share the same bandwidth simultaneously in different geographical locations, and this scheme requires only one antenna for each mobile user. Therefore, it greatly simplifies the antenna design and allocation. The goal of this dissertation is to design and implement three blind multiuser detection schemes without knowing the channel state information or the channel transfer function in the SDMA-based uplink MIMO OFDM system. The proposed scenarios include: (a) the BSS-only scheme, (b) the BSS-Minimum Mean Square Error (MMSE) scheme, and (c) the BSS-Minimum Bit Error Ratio (MBER) scheme. The major contributions of the dissertation include: (a) the three proposed schemes save the commercially expensive cost of channel resources; (b) the proposed SDMA-based uplink MIMO OFDM system simplifies the requirements of antennas for mobile users; (c) the three proposed schemes obtain high parallel computing efficiency through paralleled subcarriers; (d) the proposed BSS-MBER scheme gains the best BER performance; (e) the proposed BSS-MMSE method yields the best computational efficiency; and (f) the proposed BSS-only scenario balances the BER performance and computational complexity.

The next-generation wireless technologies are currently being researched to address the ever-increasing demands for higher data rates, massive connectivity, improved reliability, and extended coverage. Recently, massive multiple-input multiple-output (MIMO) has gained significant attention as a new physical-layer transmission technology that can achieve unprecedented spectral and energy efficiency gains via aggressive spatial multiplexing. Thus, massive MIMO has been one of the key enabling technologies for the fifth-generation and subsequent wireless standards. This dissertation thus focuses on developing a system, channel, and signal models by considering the practical wireless transmission impairments for massive MIMO systems, and ascertaining the viability of massive MIMO in fulfilling massive access, improved spectrum, enhanced security, and energy efficiency requirements. Specifically, new system and channel models, pilot sequence designs and channel estimation techniques, secure transmit/receive beamforming techniques, transmit power allocation schemes with enhanced security provisions, energy efficiency, and user fairness, and comprehensive performance analysis frameworks are developed for massive MIMO-aided non-orthogonal multiple access (NOMA), cognitive spectrum-sharing, and wireless relaying architectures.Our first work focuses on developing physical-layer transmission schemes for NOMA-aided massive MIMO systems. A spatial signature-based user-clustering and pilot allocation scheme is first formulated, and thereby, a hybrid orthogonal multiple access (OMA)/NOMA transmission scheme is proposed to boost the number of simultaneous connections. In our second work, the viability of invoking downlink pilots to boost the achievable rate of NOMA-aided massive MIMO is investigated. The third research contribution investigates the performance of underlay spectrum-sharing massive MIMO systems for reverse time division duplexing based transmission strategies, in which primary and secondary systems concurrently operate in opposite directions. Thereby, we show that the secondary system can be operated with its maximum average transmit power independent of the primary system in the limit of infinity many primary/secondary base-station antennas. In our fourth work, signal processing techniques, power allocation, and relay selection schemes are designed and analyzed for massive MIMO relay networks to optimize the trade-off among the achievable user rates, coverage, and wireless resource usage. Finally, the cooperative jamming and artificial noise-based secure transmission strategies are developed for massive MIMO relay networks with imperfect legitimate user channel information and with no channel knowledge of the eavesdropper. The key design criterion of the aforementioned transmission strategies is to efficiently combine the spatial multiplexing gains and favorable propagation conditions of massive MIMO with properties of NOMA, underlay spectrum-sharing, and wireless relay networks via efficient signal processing.