Global mobile data traffic has more than doubled in the past four years, and will only increase throughout the upcoming years. Modern cellular systems are striving to enable communications at high data rates over wide geographical areas to meet the surge in data demand. This requires advanced technologies to mitigate fundamental effects of wireless communications like path-loss, shadowing, small-scale fading, and interference. Two of such technologies are: i) deploying multiple antennas at the transmitter and receiver, and ii) employing an extra radio, called the relay, to forward messages from the transmitter to the receiver. The advantages of both technologies can be leveraged by using multiple antennas at the relay, transmitter, and receiver. Multiple-antenna relay-assisted communication is emerging as one promising technique for expanding the overall capacity of cellular networks. Taking full advantage of multiple-antenna relay-assisted cellular systems requires transmission strategies for jointly configuring the transmitters and receivers based on knowledge of the wireless propagation medium. This dissertation proposes such transmission strategies for wireless multiple-antenna relay-assisted systems. Two popular types of relays are considered: i) amplify-and-forward relays (the relays simply apply linear signal processing to their observed signals before retransmitting) and ii) decode-and-forward relays (the relays decode their observed signals and then re-encode before retransmitting). The first part of this dissertation considers the three-node multiple-antenna amplify-and-forward relay channel. Algorithms for adaptively selecting the number of data streams and subsets of transmit antennas at the transmitter and relay to provide reliable transmission at a guaranteed rate are proposed. Expressions for extracting spatial characteristics of the end-to-end multiple-antenna relay channel are derived. The second part of the dissertation presents interference management strategies that are developed specifically for two models of multiple-antenna relay interference channels where a number of relays assist multiple transmitters to communicate with multiple receivers. One model uses amplify-and-forward relays while the other uses decode-and-forward relays. Based on the idea of interference alignment, these strategies aim at maximizing the sum of achievable end-to-end rates. Simulation results show that the proposed transmission strategies with multiple-antenna relays achieve higher capacity and reliability than both those without relays and those with single-antenna relays. / text

This thesis presents the development of a Sensor Tower Platform (STP) comprised of an array of Passive Infra-Red (PIR) sensors along with a classification algorithm that enables the STP to distinguish between human intrusion, animal intrusion and clutter arising from wind-blown vegetative movement in an outdoor environment. The research was motivated by the aim of exploring the potential use of wireless sensor networks (WSNs) as an early-warning system to help mitigate human-wildlife conflicts occurring at the edge of a forest. While PIR sensors are in commonplace use in indoor settings, their use in an outdoor environment is hampered by the fact that they are prone to false alarms arising from wind-blown vegetation. Every PIR sensor is made up of one or more pairs of pyroelectric pixels arranged in a plane, and the orientation of interest in this thesis is one in which this plane is a vertical plane, i.e., a plane perpendicular to the ground plane. The intersection of the Field Of View (FOV) of the PIR sensor with a second vertical plane that lies within the FOV of the PIR sensor, is called the virtual pixel array (VPA). The structure of the VPA corresponding to the plane along which intruder motion takes place determines the form of the signal generated by the PIR sensor. The STP developed in this thesis employs an array of PIR sensors designed so as to result in a VPA that makes it easier to discriminate between human and animal intrusion while keeping to a small level false alarms arising from vegetative motion. The design was carried out in iterative fashion, with each successive iteration separated by a lengthy testing phase. There were a total of 5 design iterations spanning a total period of 14 months. Given the inherent challenges involved in gathering data corresponding to animal intrusion, the testing of the SP was carried out both using real-world data and through simulation. Simulation was carried out by developing a tool that employed animation software to simulate intruder and animal motion as well as some limited models of wind-blown vegetation. More specifically, the simulation tool employed 3-dimensional models of intruder and shrub motion that were developed using the popular animation software Blender. The simulated output signal of the PIR sensor was then generated by calculating the area of the 3-dimensional intruder when projected onto the VPA of the STP. An algorithm for efficiently calculating this to a good degree of approximation was implemented in Open Graphics Library (OpenGL). The simulation tool was useful both for evaluating various competing design alternatives as well as for developing an intuition for the kind of signals the SP would generate without the need for time-consuming and challenging animal-motion data collection. Real-world data corresponding to human motion was gathered on the campus of the Indian Institute of Science (IISc), while animal data was recorded at a dog-trainer facility in Kengeri as well as the Bannerghatta Biological Park, both located in the outskirts of Bengaluru. The array of PIR sensors was designed so as to result in a VPA that had good spatial resolution. The spatial resolution capabilities of the STP permitted distinguishing between human and animal motion with good accuracy based on low-complexity, signal-energy computations. Rejecting false alarms arising from vegetative movement proved to be more challenging. While the inherent spatial resolution of the STP was very helpful, an alternative approach turned out to have much higher accuracy, although it is computationally more intensive. Under this approach, the intruder signal, either human or animal, was modelled as a chirp waveform. When the intruder moves along a circular arc surrounding the STP, the resulting signal is periodic with constant frequency. However, when the intruder moves along a more likely straight-line path, the resultant signal has a strong chirp component. Clutter signals arising from vegetative motion does not exhibit this chirp behavior and an algorithm that exploited this difference turned in a classification accuracy in excess of 97%.