ITC/USA 2007 Conference Proceedings / The Forty-Third Annual International Telemetering Conference and Technical Exhibition / October 22-25, 2007 / Riviera Hotel & Convention Center, Las Vegas, Nevada / The accuracy of channel estimation plays a crucial role in the demodulation of data symbols sent across an unknown wireless medium. In this work a new analytical expression for the channel estimation error of a multiple input multiple output (MIMO) system is obtained when the wireless medium is continuously changing in the temporal domain. Numerical examples are provided to illustrate our findings. Space-time (ST) coding using Continuous Phase Modulation (CPM) has spectral advantages relative to linear modulations. In spite of the spectral benefits, Space-Time Trellis Codes (STTC) using the CPM implementation of Minimum Shift Keying (MSK) scheme has inherent inphase and quadrature interference, when the received complex baseband signal is the input into the matchfilter to remove the shaped sinusoid pulses. In this paper a novel optimum transmitting and detecting structure for STTC-MSK is proposed. Treating the Alamouti scheme as an outer code, each STTC MSK waveform frame is immediately followed by the orthogonal conjugate waveform frame at the transmit side. At the receiver first orthogonal wave forming is applied, then a new time-variant yet simple trellis structure of the STTC-MSK signals is developed. This STTC-MSK detector is absolutely guaranteed to be I/Q interference-free and still keeps a smaller computation load compared with STTC-QPSK. Simulations are made over quasi-static AWGN fading channel. It is shown that our detector for ST-MSK has solved the I/Q interference problem and has around 2.8 dB gain compared with the Alamouti Scheme and 3.8 dB gain for bit error rate at 5 X 10^(-3) in a 2 by 1 Multiple Input Single Output system.

Spatial modulation (SM) is a transmission technique proposed for multiple–input multiple– output (MIMO) systems, where only one transmit antenna is active at a time, offering an increase in the spectral efficiency equal to the base–two logarithm of the number of transmit antennas. The activation of only one antenna at each time instance enhances the average bit error ratio (ABER) as inter–channel interference (ICI) is avoided, and reduces hardware complexity, algorithmic complexity and power consumption. Thus, SM is an ideal candidate for large scale MIMO (tens and hundreds of antennas). The analytical ABER performance of SM is studied and different frameworks are proposed in other works. However, these frameworks have various limitations. Therefore, a closed–form analytical bound for the ABER performance of SM over correlated and uncorrelated, Rayleigh, Rician and Nakagami–m channels is proposed in this work. Furthermore, in spite of the low–complexity implementation of SM, there is still potential for further reductions, by limiting the number of possible combinations by exploiting the sphere decoder (SD) principle. However, existing SD algorithms do not consider the basic and fundamental principle of SM, that at any given time, only one antenna is active. Therefore, two modified SD algorithms tailored to SM are proposed. It is shown that the proposed sphere decoder algorithms offer an optimal performance, with a significant reduction of the computational complexity. Finally, the logarithmic increase in spectral efficiency offered by SM and the requirement that the number of antennas must be a power of two would require a large number of antennas. To overcome this limitation, two new MIMO modulation systems generalised spatial modulation (GNSM) and variable generalised spatial modulation (VGSM) are proposed, where the same symbol is transmitted simultaneously from more than one transmit antenna at a time. Transmitting the same data symbol from more than one antenna reduces the number of transmit antennas needed and retains the key advantages of SM. In initial development simple channel models can be used, however, as the system develops it should be tested on more realistic channels, which include the interactions between the environment and antennas. Therefore, a full analysis of the ABER performance of SM over urban channel measurements is carried out. The results using the urban measured channels confirm the theoretical work done in the field of SM. Finally, for the first time, the performance of SM is tested in a practical testbed, whereby the SM principle is validated.

IEEE 802.11n Wireless Local Area Networks (WLANs) employ Multiple-Input-Multiple-Output (MIMO), which significantly boosts the raw data rate at the Physical layer (PHY). But the potential of enhancing Medium Access Control (MAC) layer efficiencies by MIMO is still in its early stage and is the aim of the research in this thesis. Many existing works in this field mainly employ distributed MIMO spatial multiplexing/Multi-User Detection (MUD) technique and stream sharing to enable multiple simultaneous transmissions. Most works require synchronization among multiple transmissions, split the channel, and aim for single-hop networks. In this thesis, a novel Hybrid Carrier Sense (HCS) framework is proposed, mainly at the MAC layer to exploit the power of MIMO. HCS senses the channel availability jointly by the virtual carrier sense and physical carrier sense. HCS does not require synchronization among nodes; each node independently and locally determines when to start its transmission. HCS not only shares the channel, but also exploits the bi-directional handshakes of the wireless transmissions and increases the number of simultaneous stream transmissions. For a network with M antennas in each node, HCS can accommodate 2x(M-1) streams instead of M streams achieved by all other existing works. Moreover, HCS is aimed for multi-hop wireless ad hoc networks, in which the hidden terminal, exposed terminal, and deafness problems greatly degrade network performance. The HCS framework incorporates solutions to these problems. HCS is implemented in an NS2 network simulator and the performance evaluation shows that HCS significantly outperforms MIMO-enabled IEEE 802.11 (in which MIMO is only used for enhancing the raw data rate in the physical layer), resulting in higher aggregate throughput, packet delivery ratio and fairness in multi-hop wireless ad hoc networks. The HCS framework will be in wide use in the future generation of wireless networks and opens up more research possibilities. Some ideas in the HCS framework can be applied not only for MIMO, but also for many other techniques surveyed in this thesis; or we may combine them with HCS to further boost the network performance. / Thesis (Ph.D, Electrical & Computer Engineering) -- Queen's University, 2013-10-15 21:46:15.983

The aim of this thesis is to develop and analyse single data set (SDS) detection algorithms that can utilise the advantages of widely-spaced (statistical) multiple-input multiple-output (MIMO) radar to increase their accuracy and performance. The algorithms make use of the observations obtained from multiple space-time adaptive processing (STAP) receivers and focus on covariance estimation and inversion to perform target detection. One of the main interferers for a Doppler radar has always been the radar’s own signal being reflected off the surroundings. The reflections of the transmitted waveforms from the ground and other stationary or slowly-moving objects in the background generate observations that can potentially raise false alarms. This creates the problem of searching for a target in both additive white Gaussian noise (AWGN) and highly-correlated (coloured) interference. Traditional STAP deals with the problem by using target-free training data to study this environment and build its characteristic covariance matrix. The data usually comes from range gates neighbouring the cell under test (CUT). In non-homogeneous or non-stationary environments, however, this training data may not reflect the statistics of the CUT accurately, which justifies the need to develop SDS methods for radar detection. The maximum likelihood estimation detector (MLED) and the generalised maximum likelihood estimation detector (GMLED) are two reduced-rank STAP algorithms that eliminate the need for training data when mapping the statistics of the background interference. The work in this thesis is largely based on these two algorithms. The first work derives the optimal maximum likelihood (ML) solution to the target detection problem when the MLED and GMLED are used in a multistatic radar scenario. This application assumes that the spatio-temporal Doppler frequencies produces in the individual bistatic STAP pairs of the MIMO system are ideally synchronised. Therefore the focus is on providing the multistatic outcome to the target detection problem. It is shown that the derived MIMO detectors possess the desirable constant false alarm rate (CFAR) property. Gaussian approximations to the statistics of the multistatic MLED and GMLED are derived in order to provide a more in-depth analysis of the algorithms. The viability of the theoretical models and their approximations are tested against a numerical simulation of the systems. The second work focuses on the synchronisation of the spatio-temporal Doppler frequency data from the individual bistatic STAP pairs in the multistatic MLED scenario. It expands the idea to a form that could be implemented in a practical radar scenario. To reduce the information shared between the bistatic STAP channels, a data compression method is proposed that extracts the significant contributions of the MLED likelihood function before transmission. To perform the inter-channel synchronisation, the Doppler frequency data is projected into the space of potential target velocities where the multistatic likelihood is formed. Based on the expected structure of the velocity likelihood in the presence of a target, a modification to the multistatic MLED is proposed. It is demonstrated through numerical simulations that the proposed modified algorithm performs better than the basic multistatic MLED while having the benefit of reducing the data exchange in the MIMO radar system.

Future 5G systems and beyond are expected to implement compact and versatile antennas in highly densifi ed millimetre-wave (MMW) wireless networks. This research emphasises on the realisation of 5G antennas provided with wide bandwidth, high gain, adaptable performance, preferably conformal implementation, and feasible bulk fabrication. Ka{band (26.5{40 GHz) is selected based on recent 5G standardisation, and novel antenna geometries are developed in this work on both rigid and flexible substrates by implementing advanced techniques of frequency reconfi guration, multiple-input-multiple- output (MIMO) assembly, as well as wideband and multiband antennas and arrays. Nove lMMW wideband antennas are presented for 5G and spatial diversity at the antenna front-ends is substantially improved by deploying wideband antennas in a MIMO topology for simultaneous multiple-channel communication. However, wideband operation is often associated with efficiency degradation, which demands a more versatile approach that allows the adaptable antenna to select the operating frequency. In this research, high performance recon figurable antennas are designed for frequency selection over Ka- {band. Also, an efficient and conformal antenna front-end solution is developed, which integrates both frequency recon guration and MIMO technology. Gain of the antenna is critically important for 5G systems to mitigate high propagation losses. Antenna design with both high gain and bandwidth is challenging as wideband antennas are traditionally gain-limited, while antenna arrays deliver high gain over a narrow bandwidth. An Enhanced Franklin array model is proposed in this thesis, which aggregates multiband response with high gain performance. Furthermore, novel flexible monopole antenna and array con gurations are realised to attain high gain profi le over the complete Ka{band. These proposed 5G antennas are anticipated as potential contribution in the progress towards the realisation of future wireless networks.

In wireless networks, the exponentially increasing demands for wireless services are encountered by the scarcity of the available radio resources. More bandwidth is required for not only accommodating the increasing number of users, but also to meet the requirements of the new services such as TV on demand, wireless gaming, and mobile Internet. Non-orthogonal multiple access (NOMA) has attracted a great attention recently due to its superior spectral efficiency (SE) over orthogonal multiple access and could play a vital role in improving the capacity of future networks. In particular, power based NOMA multiplexes the users in power domain via superposition coding (SC) and allows them to access the whole spectrum simultaneously while using successive interference cancellation (SIC) at the receiver side for signal detection. Since NOMA exploits the power domain for multiple access, power allocation is vital to achieve superior SE with NOMA. Resource allocation and its optimization are general methods used to further improve the NOMA based networks performance. In this thesis, the resource allocation in the downlink NOMA system is considered and optimized for different objective functions such as the sum rate and the energy efficiency (EE). In addition, the combination of NOMA and multiple antenna is considered using linear and non-linear precoders. In all the considered cases, suboptimal power allocation schemes are proposed and compared to the numerically obtained optimal one. Results confirm that NOMA outperforms OFDMA. It also support the effectiveness of the proposed schemes as compared to the existing ones and to the optimal one. The results also reveal that using multiple antennas with NOMA can significantly enhance the overall performance. Furthermore, a NOMA-multicell scenario is considered to test the proposed schemes under the effect of intercell interference (ICI). The results prove that the proposed methods effective as compared to the optimal one at a much lower complexity.

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.

This thesis covers the investigation and application of continuous spatial models for multiple antenna signal processing. The use of antenna arrays for advanced sensing and communications systems has been facilitated by the rapid increase in the capabilities of digital signal processing systems. The wireless communications channel will vary across space as different signal paths from the same source combine and interfere. This creates a level of spatial diversity that can be exploited to improve the robustness and overall capacity of the wireless channel. Conventional approaches to using spatial diversity have centered on smart, adaptive antennas and spatial beam forming. Recently, the more general theory of multiple input, multiple output (MIMO) systems has been developed to utilise the independent spatial communication modes offered in a scattering environment.¶ Underlying any multiple antenna system is the basic physics of electromagnetic wave propagation. Whilst a MIMO system may present a set of discrete inputs and outputs, each antenna element must interact with the underlying continuous spatial field. Since an electromagnetic disturbance will propagate through space, the field at different positions in the space will be interrelated. In this way, each position in the field cannot assume an arbitrary independent value and the nature of wave propagation places a constraint on the allowable complexity of a wave-field over space. To take advantage of this underlying physical constraint, it is necessary to have a model that incorporates the continuous nature of the spatial wave-field. ¶This thesis investigates continuous spatial models for the wave-field. The wave equation constraint is introduced by considering a natural basis expansion for the space of physically valid wave-fields. This approach demonstrates that a wave-field over a finite spatial region has an effective finite dimensionality. The optimal basis for representing such a field is dependent on the shape of the region of interest and the angular power distribution of the incident field. By applying the continuous spatial model to the problem of direction of arrival estimation, it is shown that the spatial region occupied by the receiver places a fundamental limit on the number and accuracy with which sources can be resolved. Continuous spatial models also provide a parsimonious representation for modelling the spatial communications channel independent of specific antenna array configurations. The continuous spatial model is also applied to consider limits to the problem of wireless source direction and range localisation.

Design and analysis of random-like codes for various multiple-input and multiple-output communication systems are addressed in this work. Random-like codes have drawn significant interest because they offer capacity-achieving performance. We first consider the analysis and design of low-density parity-check (LDPC) codes for turbo multiuser detection in multipath CDMA channels. We develop techniques for computing the probability density function (pdf) of the extrinsic messages at the output of the soft-input soft-output (SISO) multiuser detectors as a function of the pdf of input extrinsic messages, user spreading codes, channel impulse responses, and signal-to-noise ratios. Using these techniques, we are able to accurately compute the thresholds for LDPC codes and design good irregular LDPC codes. We then apply the tools of density evolution with mixture Gaussian approximations to optimize irregular LDPC codes and to compute minimum operational signal-to-noise ratios for ergodic MIMO OFDM channels. In particular, the optimization is done for various MIMO OFDM system configurations which include different number of antennas, different channel models and different demodulation schemes. We also study the coding-spreading tradeoff in LDPC coded CDMA systems employing multiuser joint decoding. We solve the coding-spreading optimization based on the extrinsic information SNR evolution curves for the SISO multiuser detectors and the SISO LDPC decoders. Both single-cell and multi-cell scenarios will be considered. For each of these cases, we will characterize the extrinsic information for both finite-size systems and the so-called large systems where asymptotic performance results must be evoked. Finally, we consider the design optimization of irregular repeat accumulate (IRA) codes for MIMO communication systems employing iterative receivers. We present the density evolution-based procedure with Gaussian approximation for optimizing the IRA code ensemble. We adopt an approximation method based on linear programming to design an IRA code with the extrinsic information transfer (EXIT) chart matched to that of the soft MIMO demodulator.

The thesis examines the problem of joint receive and transmit beamforming for a wireless network which consists of one relay node equipped with multiple antennas. The transmitter and the receiver are single antenna systems. The communication system consists of two phases. In the first phase the transmitter sends the information symbol to the relay while in the second phase, the relay re-transmits a linearly transformed version of the vector of its received signals. The concept of general-rank beamforming is applied to this communication scheme for the case of the uplink (transmitter-relay) and downlink (relay-receiver) channel vectors being statistically independent and statistically dependent. In the general-rank beamforming approach, the multi-antenna relay multiplies the received signal vector with a general-rank complex weight matrix and re-transmits each entry of the output vector on the corresponding antenna. The thesis presents a closed form solution to the general-rank beamforming power minimization problem with proof that for statistically independent uplink and downlink channels, the general-rank beamforming approach results in a rank-one solution for the beamforming matrix. The simulation results have shown that when the generalrank beamformer is applied to the case of statistically dependent uplink and downlink channels, the general-rank beamforming technique significantly outperforms the separable receive and transmit beamforming method. / UOIT