On Connectivity of Wireless Sensor Networks with Directional Antennas.

In this paper, we investigate the network connectivity of wireless sensor networks with directional antennas. In particular, we establish a general framework to analyze the network connectivity while considering various antenna models and the channel randomness. Since existing directional antenna models have their pros and cons in the accuracy of reflecting realistic antennas and the computational complexity, we propose a new analytical directional antenna model called the iris model to balance the accuracy against the complexity. We conduct extensive simulations to evaluate the analytical framework. Our results show that our proposed analytical model on the network connectivity is accurate, and our iris antenna model can provide a better approximation to realistic directional antennas than other existing antenna models

Cooperative communication in wireless networks: algorithms, protocols and systems

Current wireless network solutions are based on a link abstraction where a single co-channel transmitter transmits in any time duration. This model severely limits the performance that can be obtained from the network. Being inherently an extension of a wired network model, this model is also incapable of handling the unique challenges that arise in a wireless medium. The prevailing theme of this research is to explore wireless link abstractions that incorporate the broadcast and space-time varying nature of the wireless channel. Recently, a new paradigm for wireless networks which uses the idea of 'cooperative transmissions' (CT) has garnered significant attention. Unlike current approaches where a single transmitter transmits at a time in any channel, with CT, multiple transmitters transmit concurrently after appropriately encoding their transmissions. While the physical layer mechanisms for CT have been well studied, the higher layer applicability of CT has been relatively unexplored. In this work, we show that when wireless links use CT, several network performance metrics such as aggregate throughput, security and spatial reuse can be improved significantly compared to the current state of the art. In this context, our first contribution is Aegis, a framework for securing wireless networks against eavesdropping which uses CT with intelligent scheduling and coding in Wireless Local Area networks. The second contribution is Symbiotic Coding, an approach to encode information such that successful reception is possible even upon collisions. The third contribution is Proteus, a routing protocol that improves aggregate throughput in multi-hop networks by leveraging CT to adapt the rate and range of links in a flow. Finally, we also explore the practical aspects of realizing CT using real systems.PhDCommittee Chair: Sivakumar, Raghupathy; Committee Member: Ammar, Mostafa; Committee Member: Ingram, Mary Ann; Committee Member: Jayant, Nikil; Committee Member: Riley, Georg

Reinforcement Learning in Self Organizing Cellular Networks

Self-organization is a key feature as cellular networks densify and become more heterogeneous, through the additional small cells such as pico and femtocells. Self- organizing networks (SONs) can perform self-configuration, self-optimization, and self-healing. These operations can cover basic tasks such as the configuration of a newly installed base station, resource management, and fault management in the network. In other words, SONs attempt to minimize human intervention where they use measurements from the network to minimize the cost of installation, configuration, and maintenance of the network. In fact, SONs aim to bring two main factors in play: intelligence and autonomous adaptability. One of the main requirements for achieving such goals is to learn from sensory data and signal measurements in networks. Therefore, machine learning techniques can play a major role in processing underutilized sensory data to enhance the performance of SONs. In the first part of this dissertation, we focus on reinforcement learning as a viable approach for learning from signal measurements. We develop a general framework in heterogeneous cellular networks agnostic to the learning approach. We design multiple reward functions and study different effects of the reward function, Markov state model, learning rate, and cooperation methods on the performance of reinforcement learning in cellular networks. Further, we look into the optimality of reinforcement learning solutions and provide insights into how to achieve optimal solutions. In the second part of the dissertation, we propose a novel architecture based on spatial indexing for system-evaluation of heterogeneous 5G cellular networks. We develop an open-source platform based on the proposed architecture that can be used to study large scale directional cellular networks. The proposed platform is used for generating training data sets of accurate signal-to-interference-plus-noise-ratio (SINR) values in millimeter-wave communications for machine learning purposes. Then, with taking advantage of the developed platform, we look into dense millimeter-wave networks as one of the key technologies in 5G cellular networks. We focus on topology management of millimeter-wave backhaul networks and study and provide multiple insights on the evaluation and selection of proper performance metrics in dense millimeter-wave networks. Finally, we finish this part by proposing a self-organizing solution to achieve k-connectivity via reinforcement learning in the topology management of wireless networks

Airborne Directional Networking: Topology Control Protocol Design

This research identifies and evaluates the impact of several architectural design choices in relation to airborne networking in contested environments related to autonomous topology control. Using simulation, we evaluate topology reconfiguration effectiveness using classical performance metrics for different point-to-point communication architectures. Our attention is focused on the design choices which have the greatest impact on reliability, scalability, and performance. In this work, we discuss the impact of several practical considerations of airborne networking in contested environments related to autonomous topology control modeling. Using simulation, we derive multiple classical performance metrics to evaluate topology reconfiguration effectiveness for different point-to-point communication architecture attributes for the purpose of qualifying protocol design elements

Association and spectrum sharing in cellular networks

Many models have been proposed to evaluate performance of cellular communication systems. However, the emergence of new technologies have changed cellular systems significantly, and requires new modeling and analysis approaches. This dissertation studies network level optimization concerning cell association and spectrum sharing. As the first contribution, the dissertation presents a framework to investigate downlink multi-antenna heterogeneous networks with flexible cell selection and shows that a simple selection bias-based cell selection criterion closely approximates more complex selection rules to maximize mean the signal-to-interference-plus-noise- ratio (SINR). Under this simpler cell selection rule, the exact expressions for coverage probability and achievable rate of a typical user are derived along with an approximation of the coverage optimal cell selection bias. In the second contribution, the dissertation considers a cellular system where users are simultaneously connected to multiple base stations (BSs) to decrease blockage sensitivity and proposes a framework to analyze the correlation in blocking among multiple links. It evaluates the gains of macro-diversity in the presence of random blockages along with the impact of the blockage size. In the third contribution, the dissertation considers spectrum sharing among millimeter wave (mmWave) operators. A two-level architecture is proposed to model a mmWave multi-operator system and the SINR and per-user rate distribution are derived in the presence of spectrum and infrastructure sharing. It is shown that due to narrow beams, license sharing among operators improves system performance by increasing the per-user rate, even when there is no explicit coordination. In the fourth contribution, this analysis is extended to include static coordination among operators in the form of secondary licensing. A framework is developed to model a mmWave cellular system with a primary operator that has an ``exclusive-use'' license with a provision to sell a restricted secondary license to another operator that has a maximum allowable interference threshold. This licensing approach provides a way of differentiating the spectrum access for the different operators. Results show that compared to uncoordinated sharing, a reasonable gain can be achieved using the proposed secondary licensing, especially for edge rates.Electrical and Computer Engineerin