Single frame ultrasound B-mode image quality is largely governed by the ability to focus the ultrasound beam over a range in depths both in transmission and reception. By developing a comprehensive understanding of acoustic wave propagation two signal processing methods were identified for solving the transmission problem. We made use of both the impulse response using the classical point spread function (PSF) and the spatial sensitivity function (SSF) which describes the spatial distribution at a particular time. Using the angular spectrum method, an accurate analytical model was developed for the field distribution arising from a finite geometry, apodized and focused, plane piston transducer. While there is a thorough understanding of the radiated field arising from uniformly excited plane piston transducers, the focused equivalent (i.e., one that allows a continuous change in phase over the plane piston surface) is incomplete and assumes the Fresnel approximation. Our model addresses the effects of diffraction and evanescent waves without the use of the Fresnel approximation and is applicable at all near- and far-field locations in a lossless medium. The model was analyzed to identify new insights into wave propagation and compared with the Fresnel approximation and the spherically-focused, concave transducer. The piston transducer model was then extended to an attenuating and dispersive medium. After analysing existing models of power-law frequency dependent attenuation, a causal, spherical wave Green’s function was derived from the Navier-Stokes equation for a classical viscous medium. Modifications to the angular spectrum method were presented and used to analyze the radiated field of a focused, planar piston transducer. Finally, after presenting our signal processing strategy for improving imaging spatial resolution through minimization of the SSF, two signal processing methods were derived and analysed in simulation: a deconvolution technique to remove the effects of the ultrasound excitation wave and suppress additive noise from the received ultrasound signal, and a retrospective transmit focusing method that changed the response from a predefined transmit focus to an arbitrary transmit focal depth. Proof-of-concept simulations were presented using a variable number of scatterers and compared with the traditional matched filtering and envelope detection technique.

Master of Engineering (Research) / Multiple antenna systems are an appealing candidate for emerging fourth-generation wireless networks due to its potential to exploit space diversity for increasing conveyed throughput without wasting bandwidth and power resources. Particularly, layered space-time architecture (LST) proposed by Foschini, is a technique to achieve a significant fraction of the theoretical capacity with a reasonable implementation complexity. There has been a great deal of challenges in the detection of space-time signal; especially to design a low-complexity detector, which can efficiently remove multi-layer interference and approach the interference free bound. The application of iterative principle to joint detection and decoding has been a promising approach. It has been shown that, the iterative receiver with parallel interference canceller (PIC) has a low linear complexity and near interference free performance. Furthermore, it is widely accepted that the performance of digital communication systems can be considerably improved once the channel state information (CSI) is used to optimize the transmit signal. In this thesis, the problem of the design of a power allocation strategy in LST architecture to simultaneously optimize coding, diversity and weighting gains is addressed. A more practical scenario is also considered by assuming imperfect CSI at the receiver. The effect of channel estimation errors in LST architecture with an iterative PIC receiver is investigated. It is shown that imperfect channel estimation at an LST receiver results in erroneous decision statistics at the very first iteration and this error propagates to the subsequent iterations, which ultimately leads to severe degradation of the overall performance. We design a transmit power allocation policy to take into account the imperfection in the channel estimation process. The transmit power of various layers is optimized through minimization of the average bit error rate (BER) of the LST architecture with a low complexity iterative PIC detector. At the receiver, the PIC detector performs both interference regeneration and cancellation simultaneously for all layers. A convolutional code is used as the constituent code. The iterative decoding principle is applied to pass the a posteriori probability estimates between the detector and decoders. The decoder is based on the maximum a posteriori (MAP) algorithms. A closed-form optimal solution for power allocation in terms of the minimum BER is obtained. In order to validate the effectiveness of the proposed schemes, substantial simulation results are provided.

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No / Multi-antenna(MIMO) techniques are reported to improve the performance of radio communication systems in terms of their capacity and spectral efficiency. In combination with appropriate receiver technologies they can also provide savings in the required transmit power with respect to target bit error rate. Long Term Evolution(LTE), one of the candidates for fourth generation(4G) mobile communication systems has MIMO as one of its underlying technologies and ITU defined channel models for its propagating environment. This paper undertakes a comprehensive verification of the performance of transmit diversity MIMO in the downlink sector of LTE. It uses models built using MATLAB to carry out simulations. It is deduced that generally increasing transmit diversity configuration from 2x2 to 4x4 offers SNR savings in flat fading channels though with a user equipment moving at 30km/hr, deploying 2x2 offers higher SNR saving below 7dB. Furthermore bandwidth variation has minimal effect on the BER performance of transmit MIMO except at SNR values above 9dB while the gains of higher modulation schemes come with a transmit power penalty.

Dynamic spectrum access (DSA) is a powerful approach to mitigate the spectrum scarcity problem caused by rapid increase in wireless communication demands. Based on architecture design, DSA systems can be categorized as centralized and distributed. To successfully enable DSA, both centralized and distributed systems have to deal with spectrum management issues including spectrum sensing, spectrum decision, spectrum sharing and spectrum mobility. Our work starts by investigating the challenges of efficient spectrum monitoring in centralized spectrum sensing. Since central controllers usually require the presence information of incumbent users/primary users (IUs) for decision making, which is obtained during spectrum sensing, privacy issues of IUs become big concerns in some DSA systems where IUs have strong operation security needs. To aid in this, we design novel location privacy protection schemes for IUs. Considering the general drawbacks of centralized systems including high computational overhead for central controllers, single point failure and IU privacy issues, in many scenarios, a distributed DSA system is required. In this dissertation, we also cope with the spectrum sharing issues in distributed spectrum management, specifically the secondary user (SU) power control problem, by developing distributed and secure transmit power control algorithms for SUs. In centralized spectrum management, the common approach for spectrum monitoring is to build infrastructures (e.g. spectrum observatories), which cost much money and manpower yet have relatively low coverage. To aid in this, we propose a crowdsourcing based spectrum monitoring system to capture the accurate spectrum utilization at a large geographical area, which leverages the power of masses of portable mobile devices. The central controller can accurately predict future spectrum utilization and intelligently schedule the spectrum monitoring tasks among mobile SUs accordingly, so that the energy of mobile devices can be saved and more spectrum activities can be monitored. We also demonstrate our system's ability to capture not only the existing spectrum access patterns but also the unknown patterns where no historical spectrum information exists. The experiment shows that our spectrum monitoring system can obtain a high spectrum monitoring coverage with low energy consumption. Environmental Sensing Capability (ESC) systems are utilized in DSA in 3.5 GHz to sense the IU activities for protecting them from SUs' interference. However, IU location information is often highly sensitive in this band and hence it is preferable to hide its true location under the detection of ESCs. As a remedy, we design novel schemes to preserve both static and moving IU's location information by adjusting IU's radiation pattern and transmit power. We first formulate IU privacy protection problems for static IU. Due to the intractable nature of this problem, we propose a heuristic approach based on sampling. We also formulate the privacy protection problem for moving IUs, in which two cases are analyzed: (1) protect IU's moving traces; (2) protect its real-time current location information. Our analysis provides insightful advice for IU to preserve its location privacy against ESCs. Simulation results show that our approach provides great protection for IU's location privacy. Centralized DSA spectrum management systems has to bear several fundamental issues, such as the heavy computational overhead for central controllers, single point failure and privacy concerns of IU caused by large amounts of information exchange between users and controllers and often untrusted operators of the central controllers. In this dissertation, we propose an alternative distributed and privacy-preserving spectrum sharing design for DSA, which relies on distributed SU power control and security mechanisms to overcome the limitations of centralized DSA spectrum management. / Doctor of Philosophy / Due to the rapid growth in wireless communication demands, the frequency spectrum is becoming increasingly crowded. Traditional spectrum allocation policy gives the unshared access of fixed bands to the licensed users, and there is little unlicensed spectrum left now to allocate to newly emerged communication demands. However, studies on spectrum occupancy show that many licensed users who own the license of certain bands are only active for a small percentage of time, which results in plenty of underutilized spectrum. Hence, a new spectrum sharing paradigm, called dynamic spectrum access (DSA), is proposed to mitigate this problem. DSA enables the spectrum sharing between different classes of users, generally, the unlicensed users in the DSA system can access the licensed spectrum opportunistically without interfering with the licensed users. Based on architecture design, DSA systems can be categorized as centralized and distributed. In centralized systems, a central controller will make decisions on spectrum usage for all unlicensed users. Whereas in distributed systems, unlicensed users can make decisions for themselves independently. To successfully enable DSA, both centralized and distributed DSA systems need to deal with spectrum management issues, such as resource allocation problems and user privacy issues, etc. The resource allocation problems include, for example, the problems to discover and allocate idle bands and the problems to control users' transmit power for successful coexistence. Privacy issues may also arise during the spectrum management process since certain information exchange is inevitable for global decision making. However, due to the Federal Communications Commission's (FCC) regulation, licensed users' privacy such as their location information must be protected in any case. As a result, dynamic and efficient spectrum management techniques are necessary for DSA users. In this dissertation, we investigate the above-mentioned spectrum management issues in both types of DSA systems, specifically, the spectrum sensing challenges with licensed user location privacy issues in centralized DSA, and the spectrum sharing problems in distributed DSA systems. In doing so, we propose novel schemes for solving each related spectrum management problem and demonstrate their efficacy through the results from extensive evaluations and simulations. We believe that this dissertation provides insightful advice for DSA users to solve different spectrum management issues for enabling DSA implementation, and hence helps in a wider adoption of dynamic spectrum sharing.

Space Time Trellis Coding (STTC) is a unique technique that combines the use of multiple transmit antennas with channel coding. This scheme provides capacity benefits in fading channels, and helps in improving the data rate and reliability of wireless communication. STTC schemes have been primarily designed assuming perfect channel estimates to be available at the receiver. However, in practical wireless systems, this is never the case. The noisy wireless channel precludes an exact characterization of channel coefficients. Even near-perfect channel estimates can necessitate huge overhead in terms of processing or spectral efficiency. This practical concern motivates the study of the impact of channel estimation errors on the design and performance of STTC. The design criteria for STTC are validated in the absence of perfect channel estimates at the receiver. Analytical results are presented that model the performance of STTC systems in the presence of channel estimation errors. Training based channel estimation schemes are the most popular choice for STTC systems. The amount of training however, increases with the number of transmit antennas used, the number of multi-path components in the channel and a decrease in the channel coherence time. This dependence is shown to decrease the performance gain obtained when increasing the number of transmit antennas in STTC systems, especially in channels with a large Doppler spread (low channel coherence time). In frequency selective channels, the training overhead associated with increasing the number of antennas can be so large that no benefit is shown to be obtained by using STTC. The amount of performance degradation due to channel estimation errors is shown to be influenced by system parameters such as the specific STTC code employed and the number of transmit and receive antennas in the system in addition to the magnitude of the estimation error. Hence inappropriate choice of system parameters is shown to significantly alter the performance pattern of STTC. The viability of STTC in practical wireless systems is thus addressed and it is shown that that channel estimation could offset benefits derived from this scheme. / Master of Science

L'objectif de cette thèse repose sur le développement et la mise en œuvre des techniques de transmission parallèle (pTx) en Imagerie par Résonance Magnétique pour homogénéiser l’excitation des spins dans le cerveau humain à ultra-haut champ. Afin de permettre des démonstrations in-vivo, un concept de sécurité conservateur mais viable est introduit pour le contrôle de la puissance de la radiofréquence (RF) transmise. Par la suite, de nouvelles méthodes de minimisation du Taux d’Absorption Spécifique local et de conception d’impulsions RF non-sélectives sont investiguées. L’impact de ces impulsions courtes et relativement peu énergétiques, appelées « kT-points », est d'abord démontré dans l’approximation des petits angles de bascule de l’aimantation. Pour cibler un éventail d’applications plus large, la conception de type kT-points est ensuite généralisée en englobant les excitations à grand angle de bascule et les inversions. Cette méthode est appliquée à l'une des séquences pondérées en T1 les plus couramment utilisées en neuro-imagerie. Les résultats ainsi obtenus à 7 Tesla sont comparés à des images acquises avec une configuration clinique à 3 Tesla. Les principes de la méthode sont ainsi validés et démonstration est faite que la transmission parallèle permet aux systèmes à très haut champ d’être aussi compétitifs en imagerie pondérée en T1. Enfin, des simplifications dans la conception globale de la pTx sont étudiées pour un meilleur rapport coût-efficacité des solutions proposées. / The focus of this thesis lies on the development, and implementation, of parallel transmission (pTx) techniques in magnetic resonance imaging for flip-angle homogenization throughout the human brain at ultra-high field. In order to allow in-vivo demonstrations, a conservative yet viable safety concept is introduced to control the absorbed radiofrequency (RF) power . Subsequently, novel methods for local SAR control and non-selective RF pulse-design are investigated. The impact of these short and energy-efficient waveforms, referred to as kT-points, is first demonstrated in the context of the small-tip-angle domain. Targeting a larger scope of applications, the kT-points design is then generalized to encompass large flip angle excitations and inversions. This concept is applied to one of the most commonly used T1-weighted sequences in neuroimaging. Results thus obtained at 7 Tesla are compared to images acquired with a clinical setup at 3 Tesla, validating the principles of the kT-points method and demonstrating that pTx-enabled ultra-high field systems can also be competitive in the context of T1-weighted imaging. Finally, simplifications in the global design of the pTx-implementation are studied in order to obtain a more cost-effective solution.

This doctoral thesis is focused into the area of multicarrier radio communications systems. These systems are perspective for current and incoming mobile communications and wireless networks. Advantages of multicarrier systems like better multipath propagation resistivity are redeemed by some disadvantages. The high peak to average power ratio of transmitted signal belongs to these disadvantages, for its inconvenience for high efficient power amplification. The thesis concerns with peak to average power ratio reduction methods for OFDM systems, that belongs to the most used multicarrier systems. One of the main objectives is the modification of the existing methods with the intention of complexity reduction. Following subject of interest is the analysis of suitable methods combinations possibilities for more significant peak to average power ratio reduction. One part of this thesis is research of influence of these methods on the OFDM signals with different parameters.

In this thesis, we consider the problem of designing space-time block codes that have low maximum-likelihood (ML) decoding complexity. We present a unified framework for determining the worst-case ML decoding complexity of space-time block codes. We use this framework to not only determine the worst-case ML decoding complexity of our own constructions, but also to show that some popular constructions of space-time block codes have lower ML decoding complexity than was previously known. Recognizing the practical importance of the two transmit and two receive antenna system, we propose the asymmetric golden code, which is designed specifically for low ML decoding complexity. The asymmetric golden code has the lowest decoding complexity compared to previous constructions of space-time codes, regardless of whether the channel varies with time. We also propose the embedded orthogonal space-time codes, which is a family of codes for an arbitrary number of antennas, and for any rate up to half the number of antennas. The family of embedded orthogonal space-time codes is the first general framework for the construction of space-time codes with low-complexity decoding, not only for rate one, but for any rate up to half the number of transmit antennas. Simulation results for up to six transmit antennas show that the embedded orthogonal space-time codes are simultaneously lower in complexity and lower in error probability when compared to some of the most important constructions of space-time block codes with the same number of antennas and the same rate larger than one. Having considered the design of space-time block codes with low ML decoding complexity on the transmitter side, we also develop efficient algorithms for ML decoding for the golden code, the asymmetric golden code and the embedded orthogonal space-time block codes on the receiver side. Simulations of the bit-error rate performance and decoding complexity of the asymmetric golden code and embedded orthogonal codes are used to demonstrate their attractive performance-complexity tradeoff.