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<p>Multi-input multi-output (MIMO) systems are expected to playa crucial role in future wireless communications and a significant increase of interest in all aspects of MIMO system design has been seen in the past decade. The primary interest of this thesis is in the receiver part of the MIMO system. In this area, continuous interest has been shown in developing blind-adaptive decoding algorithms. While blind decoding algorithms improve data throughput by enabling the system de:signer to replace training symbols with data, they also tend to perform robustly against any environment disturbances, compared to their training-based counterparts. On the other hand, considering the fact that the wireless end user environment is becoming increasingly mobile, adaptive algorithms have the ability to improve the performance of a system regardless of whether it is a blind system or a training-based one. The primary difficulty faced by blind and adaptive algorithms is that they generally are computationally intense. In this thesis, we develop semi-blind and blind decoding algorithms that are adaptive in nature as well as computationally efficient for multi-antenna systems.</p> <p>First, we consider the problem of channel tracking for MIMO communication systems where the MIMO channel is time-varying. We consider a class of MIMO systems where orthogonal space-time block codes (OSTBCs) are used as the underlying space-time coding schemes. For a general MIMO system with any number of transmitting and receiving antenna combinations, a two-step MIMO channel tracking algorithm is proposed. As the first step, Kalman filtering is used to obtain an initial channel estimate for the current block based on the channel estimates obtained for previous blocks. Then, in the second step, the so-obtained initial channel estimate is refined using a decision-directed iterative method. We show that, due to specific properties of orthogonal space-time block codes, both the Kalman filter and the decision-directed algorithm can be significantly simplified. Then, we extend the above receiver for MIMO-OFDM systems and propose a computationally efficient semi-blind receiver for MIMO systems in frequency-selective channels. Further, for the proposed receivers, we have derived theoretical performance analysis in terms of probability of error. Assuming the knowledge of the transmitted symbols for the first block, we have derived the instantaneous signal to interference and noise ratio (SINR) for consecutive transmission blocks in the absence of training, by exploiting Kalman filtering to track the channel in a decision-directed mode. Later, we extend the the theoretical performance limit comparisons for time-domain vs. frequency-domain channel tracking for MIMO-OFDM systems. Further, we study the advantage of adaptive channel tracking algorithms in comptype pilot aided channel estimation schemes in practical MIMO-OFDM systems.</p> <p>After that, an efficient sequential Monte-Carlo (SMC) algorithm is developed for blind detection in MIMO systems where OSTBCs are used as the underlying space-time coding scheme. The proposed algorithm employs Rao-Blackwellization strategy to marginalize out the (unwanted) channel coefficients and uses optimal importance function to generate samples to propagate the posterior distribution. The algorithm is blind in the sense that, unlike the earlier ones, the transmission of training symbols is not required by this scheme. The marginalization involves the computation of (hundreds of) Kalman filters running in parallel resulting in intense computer requirement. We show that, the marginalization step can be significantly simplified for the speci1ied problem under no additional assumptions - resulting in huge computational savings. Further, we extend this result to frequency selective channels and propose a novel and efficient SMC receiver for MIMO-OFDM systems.</p> <p>Finally, a novel adaptive algorithm is presented for directional MIMO systems. Specifically, the problem of direction of arrivall (DOA) tracking of an unknown time-varying number of mobile sources is considered. The challenging part of the problem is the unknown, time-varying number of sources that demand a combination of source enumeration techniques and sequential state estimation methods to track the time-varying number of DOAs. In this thesis, we transform the problem into a novel state-space model, and, by employing probability hypothesis density (PHD) filtering technique, propose a simple algorithm that is able to track the number of sources as well as the corresponding directions of arrivals. In addition to the fact that the proposed algorithm is simple and easier to implement, simulation results show that, the PHD implementation yields superior performance over competing schemes in tracking rapidly varying number of targets.</p> / Doctor of Philosophy (PhD)

No-vision walking is supported in the central nervous system (CNS) by a spatial updating process. This process involves the iterative updating of a mental representation of the environment using estimates of distance traveled gleaned from locomotive kinematic activity. An effective means of examining the online regulation of this process is a continuous pointing task, which requires performers to walk along a straight-line forward trajectory while keeping their right arm straight and index finger fixated on a stationary ground-level target beside the walking path. In the current thesis, no-vision continuous pointing was examined in typically calibrated and recalibrated perceptual-motor states. Shoulder and trunk joint angles provided the basis for perceptual measures that reflected spatial updating performance and kinematic measures that reflected its underlying CNS online regulation. In the typically calibrated conditions, no-vision walking demonstrated a slight perceptual underestimation of distance traveled (Study 1). In the recalibrated conditions, no-vision walking demonstrated: a) perceptual underestimation and overestimation following adaptation periods involving walking with low and high visual gains, respectively (Study 2); and b) partial recalibration following exposures to vision and arm gains (Study 3). The latter was suggested as being impacted by task specific changes in CNS multisensory integration resulting from the development of a robust task prior and/or the altering of sensory cue weights. Importantly, this thesis used a novel trajectory parsing procedure to quantify discrete CNS perceptual updating units in the shoulder plane of elevation trajectory. The starts and ends of these updating units were consistently timed to the late left-to-early right foot swing phase of the step-cycle, regardless of perceptual-motor state. This was suggested to reflect perceptual units that were purposely timed, but indirectly mapped, to this kinematic event. The perceptual differences in Studies 1 and 2 were at least partially reflected in these units. / Thesis / Doctor of Philosophy (PhD) / It is well understood that humans can effectively walk without vision to environmental locations up to 15 metres away. However, less is known about how these walking movements are controlled during the course of forward progression. This thesis fills this knowledge gap using a task that requires participants to walk forward along a straight path while keeping their right index finger pointed toward a ground-level target beside the walking path. The patterns of arm movements performed during this task are indicative of the control strategies used by the performer to mentally update their positions in space. One of the key contributions of this work is showing that humans perform this mental updating in a repetitive manner, and that these repetitions are consistently linked to early forward movements of the right leg. This pattern is maintained when walking without vision is performed in a variety of different contexts.

<p> In this thesis, we present a novel signalling scheme for blind channel identification of Alamouti space-time coded (STBC) channel and a space-time coded multiple-input single-output (MISO) system under flat fading environment. By using p-ary and q-ary PSK signals (where p and q are co-prime integers), we prove that a) under a noise-free environment, only two distinct pairs of symbols are needed to uniquely decode the signal and identify the channel, and b) under complex Gaussian noise, if the pth and qth order statistics of the received signals are available, the channel coefficients can also be uniquely determined. In both cases, simple closed-form solutions are derived by exploiting specific properties of the Alamouti STBC code and linear Diophantine equation theory.</p> <p> When only a limited number of received data are available, under Gaussian noise environment, we suggest the use of the semi-definite relaxation method and/or the sphere decoding method to implement blind ML detection so that the joint estimation of the channel and the transmitted symbols can be efficiently facilitated. Simulation results show that blind ML detection methods with our signalling scheme provide superior normalized mean square error in channel estimation compared to the method using only one constellation and that the average symbol error rate is close to that of the coherent detector (which necessitates perfect channel knowledge at the receiver), particularly when the SNR is high.</p> / Thesis / Master of Applied Science (MASc)