One of the amazing properties of human vision is its ability to feel the depth of the scenes being viewed. This is made possible by a process named stereopsis, which is the ability of our brain to fuse together the stereo image pair seen by two eyes. As a stereo image pair is a direct result of the same scene being viewed by a slightly different perspective they open up a new paradigm where spatial redundancy could be exploited for efficient transmission and storage of stereo image data. This thesis introduces three novel algorithms for stereo image compression. The first algorithm improves compression by exploiting the redundancies present in the so-called disparity field of a stereo image pair. The second algorithm uses a pioneering block coding strategy to simultaneously exploit the inter-frame and intra-frame redundancy of a stereo image pair, eliminating the need of coding the disparity field. The basic idea behind the development of the third algorithm is the efficient exploitation of redundancy in smoothly textured areas that are present in both frames, but are relatively displaced from each other due to binocular parallax. Extra compression gains of up to 20% have been achieved by the use of these techniques. The thesis also includes research work related to the improvement of the MPEG-4 video coding standard, which is the first audiovisual representation standard that understands a scene as a composition of audio-visual objects. A linear extrapolation based padding technique that makes use of the trend of pixel value variation often present near object boundaries, in padding the exterior pixels of the reference video object has been proposed. Coding gains of up to 7% have been achieved for coding boundary blocks of video objects. Finally a contour analysis based approach has been proposed for MPEG-4 video object extraction.

Wireless designers constantly seek to improve the spectrum efficiency/capacity, coverage of wireless networks, and link reliability. Space-time wireless technology that uses multiple antennas along with appropriate signalling and receiver techniques offers a powerful tool for improving wireless performance. Some aspects of this technology have already been incorporated into various wireless network and cellular mobile standards. More advanced MIMO techniques are planned for future mobile networks, wireless local area network (LANs) and wide area network (WANs). Multiple antennas when used with appropriate space-time coding (STC) techniques can achieve huge performance gains in multipath fading wireless links. The fundamentals of space-time coding were established in the context of space-time Trellis coding by Tarokh, Seshadri and Calderbank. Alamouti then proposed a simple transmit diversity coding scheme and based on this scheme, general space-time block codes were further introduced by Tarokh, Jafarkhani and Calderbank. Since then space-time coding has soon evolved into a most vibrant research area in wireless communications. Recently, space-time block coding has been adopted in the third generation mobile communication standard which aims to deliver true multimedia capability. Space-time block codes have a most attractive feature of the linear decoding/detection algorithms and thus become the most popular among different STC techniques. The decoding of space-time block codes, however, requires knowledge of channels at the receiver and in most publications, channel parameters are assumed known, which is not practical due to the changing channel conditions in real communication systems. This thesis is mainly concerned with space-time block codes and their performances. The focus is on signal detection and channel estimation for wireless communication systems using space-time block codes. We first present the required background materials, discuss different implementations of space-time block codes using different numbers of transmit and receive antennas, and evaluate the performances of space-time block codes using binary phase-shift keying (BPSK), quadrature phase-shift keying (QPSK), and quadrature amplitude modulation (QAM). Then, we investigate Tarokh’s joint detection scheme with no channel state information thoroughly, and also propose a new general joint channel estimation and data detection scheme that works with QPSK and 16-QAM and different numbers of antennas. Next, we further study Yang’s channel estimation scheme, and expand this channel estimation scheme to work with 16-QAM modulation. After dealing with complex signal constellations, we subsequently develop the equations and algorithms of both channel estimation schemes to further test their performances when real signals are used (BPSK modulation). Then, we simulate and compare the performances of the two new channel estimation schemes when employing different number of transmit and receive antennas and when employing different modulation methods. Finally, conclusions are drawn and further research areas are discussed.

In this paper, an idea of using space-time block coding (STBC) in multi-user cooperative diversity has been exploited to improve the performance of the transmission in wireless local area networks. The theoretical and simulation results show that, using STBC approaches can always achieve the better performance than existing techniques without introducing the space-time coding. By analyzing the throughput and frame error ratio (FER) of the two different STBC cooperative schemes, we find the trade-off between throughput and reliability. The location of the relay is crucial to the performance, which supposes a rule for future crosslayer design.

<p> This thesis investigates the application of multiple model estimation algorithms to the problem of channel equalization for digital data transmission and channel tracking for space-time block coded systems with non-Gaussian additive noise. Recently, a network of Kalman filters (NKF) has been reported for the equalization of digital communication channels based on the approximation of the a posteriori probability density function of a sequence of delayed symbols by a weighted Gaussian sum. A serious drawback of this approach is that the number of Gaussian terms in the sum increases exponentially through iterations. In this thesis, firstly, we have shown that the NKF-based equalizer can be further improved by considering the interactions between the parallel filters in an efficient way. To this end, we take resort to the Interacting Multiple Model (IMM) estimator widely used in the area of multiple target tracking. The IMM is a very effective approach when the system exhibits discrete uncertainties in the dynamic or measurement model as well as continuous uncertainties in state values. A computationally feasible implementation based on a weighted sum of Gaussian approximation of the density functions of the data signals is introduced. Next, we present an adaptive multiple model blind equalization algorithm based on the IMM estimator to estimate the channel and the transmitted sequence corrupted by intersymbol interference and noise. It is shown through simulations that the proposed IMM-based equalizer offers substantially improved performance relative to the blind equalizer based on a (static or non-interacting) network of extended Kalman filters. It obviates the exponential growth of the state complexity caused by increasing channel memory length. The proposed approaches avoid the exponential growth of the number of terms used in the weighted Gaussian sum approximation of the plant noise making it practical for real-time processing.</p> <p> Finally, we consider the problem of channel estimation and tracking for space-time block coded systems contaminated by additive non-Gaussian noise. In many practical wireless channels in which space-time block coding techniques may be applied, the ambient noise is likely to have an impulsive component that gives rise to larger tail probabilities than is predicted by the Gaussian model. Although Kalman filters are often used in practice to track the channel variation, they are notoriously sensitive to heavy-tailed outliers and model mismatches resulting from the presence of impulsive noise. Non-Gaussian noise environments require the modification of standard filters to perform acceptably. Based on the coding/decoding technique, we propose a robust IMM algorithm approach in estimating time-selective fading channels when the measurements are perturbed by the presence of impulsive noise. The impulsive noise is modeled by a two terms Gaussian mixture distribution. Simulations demonstrate that the proposed method yields substantially improved performance compared to the conventional Kalman filter algorithm using the clipping or localization approaches to handle impulses in the observation. It is also shown that IMM-based approach performs robustly even when the prior information about the impulsive noise is not known exactly.</p> / Thesis / Master of Applied Science (MASc)

ITC/USA 2015 Conference Proceedings / The Fifty-First Annual International Telemetering Conference and Technical Exhibition / October 26-29, 2015 / Bally's Hotel & Convention Center, Las Vegas, NV / Fading estimation in wireless communication systems depend on an expected fading model and assumptions about the channel itself. The bit error rate (BER) performance of the communication system is affected by how closely the assumptions made in de- signing the estimation technique match the deployment environment. Any unforeseen disturbances or hindrances in the environment deteriorate the BER performance of the system when the estimation system is not designed to combat the same. To combat such obstacles, estimation techniques must either be reinforced with modular systems which combat such observed types of disturbances, or be redesigned as a whole considering such observations of disturbances. In this paper a modular scheme to detect and combat sinusoidal variation in fading power is developed and tested by employing the developed scheme in a multiple-input-multiple-output (MIMO) wireless communication system which adopts Space-Time Block Coding (STBC) techniques.

The design and analysis of various distributed space time block coding schemes for cooperative relay networks is considered in this thesis. Rayleigh frequency flat and selective fading channels are assumed to model the links in the networks, and interference suppression techniques together with an orthogonal frequency division multiplexing (OFDM) type transmission approach are employed to mitigate synchronization errors at the destination node induced by the different delays through the relay nodes. Closed-loop space time block coding is first considered in the context of decode-and-forward (regenerative) networks. In particular, quasi orthogonal and extended orthogonal coding techniques are employed for transmission from four relay nodes and parallel interference cancellation detection is exploited to mitigate synchronization errors. Availability of a direct link between the source and destination nodes is studied. Outer coding is then added to gain further improvement in end-to-end performance and amplify-and-forward (non regenerative) type networks together with distributed space time coding are considered to reduce relay node complexity. A novel detection scheme is then proposed for decode-and-forward and amplify-and-forward networks with closed-loop extended orthogonal coding and closed-loop quasi-orthogonal coding which reduce the computational complexity of the parallel interference cancellation. The near-optimum detector is presented for relay nodes with single or dual antennas. End-to-end bit error rate simulations confirm the potential of the approach and its ability to mitigate synchronization errors.

In recent years, relay communication has been proved to achieve the transmis-sion diversity order with space-time block coding (STBC). Most research assumedthat the relay nodes are in perfect synchronization. However, in actual, becausethe transmitting time at each relay is different, the signals from different relays received at destination will interfere with each other. Inter-symbol interference (ISI) iscaused. Besides, the time synchronization error will reduce orthogonality of space-time block coding and result in serious performance degrade. This thesis proposes a time delay compensation method by using Fourier transform and Least Square(LS)estimation method. The destination node can utilize the estimated time delay tosynchronize the received signal. Then, the space-time coding will maintain orthogonality at the receiver. Simulation results show that the proposed method caneffectively improve the performance of cooperative networks when imperfect timesynchronization exists.

Wireless systems are rapidly developing to provide high speed voice, text and multimedia messaging services which were traditionally offered by wire line networks. To support these services, channels with large capacities are required. Information theoretic investigations have shown that Multiple Input Multiple Output (MIMO) channels can achieve very high capacities. Space-Time Block Coding (STBC) and Bell Labs Layered Space-Time Architecture (BLAST) are two potential schemes which utilize the diversity offered by MIMO channels to provide reliable high date rate wireless communication. This work studies the sensitivity of these two schemes to spatial correlation in MIMO channels. The first part of the thesis studies the effect of spatial correlation on the performance of STBC by using Geometrically Based Single Bounce MIMO (GBSB-MIMO) channel models. Performance is analyzed for two scenarios: one without scatterers in the vicinity of the transmitter and other with scatterers. In the second part of the thesis, the sensitivity of BLAST to spatial correlation is analyzed. Later, schemes which use the principles of Multilayered Space-Time Coded Modulation to combine the benefits of BLAST and STBC are introduced and their performance is investigated in correlated and uncorrelated Rayleigh fading. Results indicate that schemes using orthogonal design space-time block codes are reasonably robust to spatial correlation while schemes like BLAST are very sensitive as they depend on array processing to separate signals from various transmit antennas. / Master of Science

Wireless communication systems is a well-researched area in electrical engineering that has continually evolved over the past decades. This constant evolution and development have led to well-formulated theoretical baselines in terms of reliability and efficiency. However, most communication baselines are derived by splitting the baseband communications into a series of modular blocks like modulation, coding, channel estimation, and orthogonal frequency modulation. Subsequently, these blocks are independently optimized. Although this has led to a very efficient and reliable process, a theoretical verification of the optimality of this design process is not feasible due to the complexities of each individual block. In this work, we propose two modifications to these conventional wireless systems. First, with the goal of designing better space-time block codes for improved reliability, we propose to redesign the transmit and receive blocks of the physical layer. We replace a portion of the transmit chain - from modulation to antenna mapping with a neural network. Similarly, the receiver/decoder is also replaced with a neural network. In other words, the first part of this work focuses on jointly optimizing the transmit and receive blocks to produce a set of space-time codes that are resilient to Rayleigh fading channels. We compare our results to the conventional orthogonal space-time block codes for multiple antenna configurations. The second part of this work investigates the possibility of designing a distributed multiagent reinforcement learning-based multi-access algorithm for machine type communication. This work recognizes that cellular networks are being proposed as a solution for the connectivity of machine type devices (MTDs) and one of the most crucial aspects of scheduling in cellular connectivity is the random access procedure. The random access process is used by conventional cellular users to receive an allocation for the uplink transmissions. This process usually requires six resource blocks. It is efficient for cellular users to perform this process because transmission of cellular data usually requires more than six resource blocks. Hence, it is relatively efficient to perform the random access process in order to establish a connection. Moreover, as long as cellular users maintain synchronization, they do not have to undertake the random access process every time they have data to transmit. They can maintain a connection with the base station through discontinuous reception. On the other hand, the random access process is unsuitable for MTDs because MTDs usually have small-sized packets. Hence, performing the random access process to transmit such small-sized packets is highly inefficient. Also, most MTDs are power constrained, thus they turn off when they have no data to transmit. This means that they lose their connection and can't maintain any form of discontinuous reception. Hence, they perform the random process each time they have data to transmit. Due to these observations, explicit scheduling is undesirable for MTC. To overcome these challenges, we propose bypassing the entire scheduling process by using a grant free resource allocation scheme. In this scheme, MTDs pseudo-randomly transmit their data in random access slots. Note that this results in the possibility of a large number of collisions during the random access slots. To alleviate the resulting congestion, we exploit a heterogeneous network and investigate the optimal MTD-BS association which minimizes the long term congestion experienced in the overall cellular network. Our results show that we can derive the optimal MTD-BS association when the number of MTDs is less than the total number of random access slots. / Master of Science / Wireless communication systems is a well researched area of engineering that has continually evolved over the past decades. This constant evolution and development has led to well formulated theoretical baselines in terms of reliability and efficiency. This two part thesis investigates the possibility of improving these wireless systems with machine learning. First, with the goal of designing more resilient codes for transmission, we propose to redesign the transmit and receive blocks of the physical layer. We focus on jointly optimizing the transmit and receive blocks to produce a set of transmit codes that are resilient to channel impairments. We compare our results to the current conventional codes for various transmit and receive antenna configuration. The second part of this work investigates the possibility of designing a distributed multi-access scheme for machine type devices. In this scheme, MTDs pseudo-randomly transmit their data by randomly selecting time slots. This results in the possibility of a large number of collisions occurring in the duration of these slots. To alleviate the resulting congestion, we employ a heterogeneous network and investigate the optimal MTD-BS association which minimizes the long term congestion experienced in the overall network. Our results show that we can derive the optimal MTD-BS algorithm when the number of MTDs is less than the total number of slots.

The GSM platform is a extremely successful wireless technology and an unprecedented story of global achievement. The GSM platform is growing and evolving and offers an expanded and feature-rich voice and data enabling services. General Packet Radio Service, (GPRS), will have a tremendous transmission rate, which will make a significant impact on most of the existing services. Additionally, GPRS stands ready for the introduction of new services as operators and users, both business and private, appreciate the capabilities and potential that GPRS provides. Services such as the Internet, videoconferencing and on-line shopping will be as smooth as talking on the phone. Moreover, the capability and ease of access to these services increase at work, at home or during travel. In this research the traffic channel of a GSM system was studied in detail and simulated in order to obtain a performance analysis. Matlab, software from Mathworks, was used for the simulation. Both the forward and the reverse links of a GSM system were simulated. A flat fading model was used to model the channel. Signal to Noise Ratio, (SNR), was the primary metric that was varied during the simulation. All the building blocks for a traffic channel, including a Convolutional encoder, an Interleaver and a Modulator were coded in Matlab. Finally the GPRS system, which is an enhancement of the GSM system for data services was introduced.